OPM Data Separation Analysis

Christopher Boomhower1, Stacey Fabricant2, Alex Frye1, David Mumford2, Michael Smith1, Lindsay Vitovsky1

1 Southern Methodist University, Dallas, TX, US 2 Penn Mutual Life Insurance Co, Horsham PA </i></b>

Introduction

background text...

our intent is to: 1)..2)...3)........

In [ ]:
 

Data Understanding

Data Source Background Text & citation links

Dataset Attribute Descriptions

Load the Data

To begin our analysis, we need to load the data from our 89 source .txt files. Data is separated into two separate groups of files; Separation and Non-Separation, thus data is loaded in two separate phases, then unioned together. Once data is loaded, Steps taken to remove non-US observations or those with no specified occupation, no specified salary, or no specified length of service level. Of a total 8,423,336 observations, we end with 8,232,375 after removal of these observations.

In [1]:
## Import libraries
import pickle
import os
import psutil
import glob
import pandas as pd
import numpy as np
from IPython.display import display
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import requests
import json
import missingno as msno
import prettytable
import math
from sklearn.preprocessing import MinMaxScaler, StandardScaler, label_binarize
from sklearn.multiclass import OneVsRestClassifier
from sklearn.utils import class_weight
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import StratifiedKFold
from sklearn.cross_validation import cross_val_score
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_auc_score
from scipy import interp
from sklearn.metrics import confusion_matrix
from sklearn.ensemble  import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import ShuffleSplit
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score
from datetime import datetime
from dateutil.parser import parse
from itertools import cycle
from sklearn import metrics as mt
from sklearn.feature_selection import chi2
import itertools

#Need to make sure you install the rpy2 package via following command in the Putty genuse41 console:
#python3 /usr/bin/pip install --user rpy2
#NOTE: If the above pip install does not work, try the following instead:
#python3 /usr/local/es7/lib/python3.5/site-packages/pip install --user rpy2
%load_ext rpy2.ipython
from rpy2.robjects import pandas2ri


## Library Options

pd.options.mode.chained_assignment = None

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
/usr/local/es7/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
In [2]:
## Pre-defined Functions for use later
def pickleObject(objectname, filename, filepath = "PickleJar/"):
    fullpicklepath = "{0}{1}.pkl".format(filepath, filename)
    # Create a variable to pickle and open it in write mode
    picklefile = open(fullpicklepath, 'wb')
    pickle.dump(objectname, picklefile)
    picklefile.close()
    
def unpickleObject(filename, filepath = "PickleJar/"):
    fullunpicklepath = "{0}{1}.pkl".format(filepath, filename)
    # Create an variable to pickle and open it in write mode
    unpicklefile = open(fullunpicklepath, 'rb')
    unpickleObject = pickle.load(unpicklefile)
    unpicklefile.close()

    return unpickleObject
    
def clear_display():
    from IPython.display import clear_output
    
## Pre-defined variables for use later
dataOPMPath = "dataOPM"
dataEMPPath = "dataEMP"
PickleJarPath = "PickleJar"
In [3]:
%%time

## Load OPMSeparation Files

OPMDataFiles = glob.glob(os.path.join(dataOPMPath, "*.txt"))

for i in range(0,len(OPMDataFiles)):
    OPMDataFiles[i] = OPMDataFiles[i].replace("\\","/")

OPMDataList = []

for i,j in zip(OPMDataFiles,range(0,len(OPMDataFiles))):
    OPMDataList.append(pd.read_csv(i, dtype = 'str'))
    display(OPMDataList[j].head())

## Load the SEPDATA_FY2015 file into it's own object
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/SEPDATA_FY2015.txt']
OPMDataOrig = OPMDataList[indexes[0]]
AGELVL AGELVLT
0 A Less than 20
1 B 20-24
2 C 25-29
3 D 30-34
4 E 35-39
AGYTYP AGYTYPT AGY AGYT AGYSUB AGYSUBT
0 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF** AF**-INVALID
1 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF02 AF02-AIR FORCE INSPECTION AGENCY (FO)
2 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF03 AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION...
3 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF06 AF06-AIR FORCE AUDIT AGENCY
4 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF07 AF07-AIR FORCE OFFICE OF SPECIAL INVESTIGATIONS
QTR QTRT EFDATE EFDATET
0 1 OCT-DEC 2014 201410 OCT 2014
1 1 OCT-DEC 2014 201411 NOV 2014
2 1 OCT-DEC 2014 201412 DEC 2014
3 2 JAN-MAR 2015 201501 JAN 2015
4 2 JAN-MAR 2015 201502 FEB 2015
GENDER GENDERT
0 F Female
1 M Male
2 Z Unspecified
GSEGRD
0 **
1 01
2 02
3 03
4 04
LOCTYP LOCTYPT LOC LOCT
0 1 United States 01 01-ALABAMA
1 1 United States 02 02-ALASKA
2 1 United States 04 04-ARIZONA
3 1 United States 05 05-ARKANSAS
4 1 United States 06 06-CALIFORNIA
LOSLVL LOSLVLT
0 A Less than 1 year
1 B 1 - 2 years
2 C 3 - 4 years
3 D 5 - 9 years
4 E 10 - 14 years
OCCTYP OCCTYPT OCCFAM OCCFAMT OCC OCCT
0 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS 0006 0006-CORRECTIONAL INSTITUTION ADMINISTRATION
1 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS 0007 0007-CORRECTIONAL OFFICER
2 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS 0017 0017-EXPLOSIVES SAFETY
3 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS 0018 0018-SAFETY AND OCCUPATIONAL HEALTH MANAGEMENT
4 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS 0019 0019-SAFETY TECHNICIAN
PATCO PATCOT
0 1 Professional
1 2 Administrative
2 3 Technical
3 4 Clerical
4 5 Other White Collar
PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT PPGRD
0 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GL GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... GL-03
1 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GL GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... GL-04
2 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GL GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... GL-05
3 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GL GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... GL-06
4 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GL GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... GL-07
SALLVL SALLVLT
0 A Less than $20,000
1 B $20,000 - $29,999
2 C $30,000 - $39,999
3 D $40,000 - $49,999
4 E $50,000 - $59,999
SEP SEPT
0 SA Transfer Out - Individual Transfer
1 SB Transfer Out - Mass Transfer
2 SC Quit
3 SD Retirement - Voluntary
4 SE Retirement - Early Out
TOATYP TOATYPT TOA TOAT
0 1 Permanent 10 10-Competitive Service - Career
1 1 Permanent 15 15-Competitive Service - Career-Conditional
2 1 Permanent 30 30-Excepted Service - Schedule A
3 1 Permanent 32 32-Excepted Service - Schedule B
4 1 Permanent 34 34-Excepted Service - Schedule C
WSTYP WSTYPT WORKSCH WORKSCHT
0 1 Full-time B B-Full-time Nonseasonal Baylor Plan
1 1 Full-time F F-Full-time Nonseasonal
2 1 Full-time G G-Full-time Seasonal
3 1 Full-time H H-Full-time On-call
4 2 Not Full-time I I-Intermittent Nonseasonal
AGYSUB SEP EFDATE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS
0 AA00 SC 201507 C M 11 A 11 0905 1 GS-11 F 40 F 1 063722 00.8
1 AA00 SD 201509 K M NaN D 11 0301 2 EX-02 Z 46 F 1 NaN 08.1
2 AA00 SC 201506 D F 15 C 11 0905 1 GS-15 L 30 F 1 126245 04.8
3 AF** SA 201503 H M 11 C 48 2210 2 GS-11 F 10 F 1 066585 04.9
4 AF02 SD 201506 I M 15 J 35 0301 2 GS-15 O 10 F 1 156737 39.8
CPU times: user 342 ms, sys: 43 ms, total: 385 ms
Wall time: 386 ms
In [4]:
%%time

#print(OPMDataFiles)

print(len(OPMDataOrig))

##### Merge / Modify Codes / Aggregate Attributes to be more descriptive per the metadata files

OPMDataMerged = OPMDataOrig.copy()

##AGYSUB - AGYTYP, AGY
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTagy.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'AGYSUB', how = 'left')

##EFDate - quarter, month
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTefdate.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'EFDATE', how = 'left')

##AGELVL - AGELVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTagelvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'AGELVL', how = 'left')

##LOSLVL - LOSLVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTloslvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'LOSLVL', how = 'left')

##LOC - LocTypeT, LocT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTloc.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'LOC', how = 'left')

##OCC - OCCTYPT, OCCFAM
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTocc.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'OCC', how = 'left')

##PATCO - PATCOT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTpatco.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'PATCO', how = 'left')

##PPGRD - PayPlan, PPGroup, PPTYP
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTppgrd.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'PPGRD', how = 'left')

##SALLVL - SALLVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTsallvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'SALLVL', how = 'left')

##TOA - TOATYP
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTtoa.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'TOA', how = 'left')

##WORKSCH - WSTYPT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTwrksch.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'WORKSCH', how = 'left')


## Modify Data Types for numeric objects
OPMDataMerged["SALARY"] = OPMDataMerged["SALARY"].apply(pd.to_numeric)
OPMDataMerged["COUNT"]  = OPMDataMerged["COUNT"].apply(pd.to_numeric)
OPMDataMerged["LOS"]    = OPMDataMerged["LOS"].apply(pd.to_numeric)

print("Original SEP data size of: "+str(len(OPMDataMerged)))
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["LOCTYP"] != "1"]))+" Non-US observations.")

## Remove Non-US Data
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOCTYP"] == "1"]

print("Removing "+str(len(OPMDataMerged[OPMDataMerged["OCCTYP"] == "3"]))+" observations with no specified Occupation.")

   ## Remove Observations with no specified occupation
OPMDataMerged = OPMDataMerged[OPMDataMerged["OCCTYP"] != "3"]

print("Removing "+str(len(OPMDataMerged[OPMDataMerged["SALLVL"] == "Z"]))+" observations with no specified Salary.")

   ## Remove Observations with no specified salary
OPMDataMerged = OPMDataMerged[OPMDataMerged["SALLVL"] != "Z"]

print("Removing "+str(len(OPMDataMerged[OPMDataMerged["LOSLVL"] == "Z"]))+" observations with no specified Length of Service.")

   ## Remove Observations with no specified LOSLVL
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOSLVL"] != "Z"]

print("Removing "+str(len(OPMDataMerged[OPMDataMerged["AGELVL"] == "A"]))+" observations of Age Level A")

## Remove Observations from Age Level A (less than 20 years old)
OPMDataMerged = OPMDataMerged[OPMDataMerged["AGELVL"] != "A"]

print("Removing "+str(len(OPMDataMerged[OPMDataMerged["AGELVL"] == "Z"]))+" observations with no specified Age Level.")

   ## Remove Observations with no specified Age Level
OPMDataMerged = OPMDataMerged[OPMDataMerged["AGELVL"] != "Z"]

    ## Fix differences in spaces on WORKSCHT Column
OPMDataMerged["WORKSCHT"] = np.where(OPMDataMerged["WORKSCHT"].str[0]=="F", 'Full-time Nonseasonal',
                                np.where(OPMDataMerged["WORKSCHT"].str[0]=="I", 'Intermittent Nonseasonal',
                                         np.where(OPMDataMerged["WORKSCHT"].str[0]=="P", 'Part-time Nonseasonal',
                                                  np.where(OPMDataMerged["WORKSCHT"].str[0]=="G", 'Full-time Seasonal',
                                                        np.where(OPMDataMerged["WORKSCHT"].str[0]=="J", 'Intermittent Seasonal',
                                                                np.where(OPMDataMerged["WORKSCHT"].str[0]=="Q", 'Part-time Seasonal',
                                                                        np.where(OPMDataMerged["WORKSCHT"].str[0]=="T", 'Part-time Job Sharer Seasonal',
                                                                                np.where(OPMDataMerged["WORKSCHT"].str[0]=="S", 'Part-time Job Sharer Nonseasonal',
                                                                                        np.where(OPMDataMerged["WORKSCHT"].str[0]=="B", 'Full-time Nonseasonal Baylor Plan',
                                                                                                'NO WORK SCHEDULE REPORTED' ### ELSE case represents Night
                                                                                                 )
                                                                                         )
                                                                                 )
                                                                         )
                                                                 )
                                                          )
                                                 )
                                        )
                               )    

display(OPMDataMerged.head())
print("New SEP data size of: "+str(len(OPMDataMerged)))
display(OPMDataMerged.describe().transpose())
#del OPMDataList,OPMDataFiles
226357
Original SEP data size of: 226357
Removing 8021 Non-US observations.
Removing 55 observations with no specified Occupation.
Removing 1426 observations with no specified Salary.
Removing 3 observations with no specified Length of Service.
Removing 2570 observations of Age Level A
Removing 0 observations with no specified Age Level.
AGYSUB SEP EFDATE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR QTRT EFDATET AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT
0 AA00 SC 201507 C M 11 A 11 0905 1 GS-11 F 40 F 1 63722.0 0.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 4 JUL-SEP 2015 JUL 2015 25-29 Less than 1 year 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 2 Non-permanent 40-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal
2 AA00 SC 201506 D F 15 C 11 0905 1 GS-15 L 30 F 1 126245.0 4.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 3 APR-JUN 2015 JUN 2015 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $120,000 - $129,999 1 Permanent 30-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal
3 AF** SA 201503 H M 11 C 48 2210 2 GS-11 F 10 F 1 66585.0 4.9 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF**-INVALID 2 JAN-MAR 2015 MAR 2015 50-54 3 - 4 years 1 United States 48-TEXAS 1 White Collar 22 22xx-INFORMATION TECHNOLOGY 2210-INFORMATION TECHNOLOGY MANAGEMENT Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal
4 AF02 SD 201506 I M 15 J 35 0301 2 GS-15 O 10 F 1 156737.0 39.8 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF02-AIR FORCE INSPECTION AGENCY (FO) 3 APR-JUN 2015 JUN 2015 55-59 35 years or more 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $150,000 - $159,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal
5 AF03 SC 201509 H M 13 B 06 0301 2 GS-13 I 15 F 1 92973.0 1.0 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... 4 JUL-SEP 2015 SEP 2015 50-54 1 - 2 years 1 United States 06-CALIFORNIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $90,000 - $99,999 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal
New SEP data size of: 214282
count mean std min 25% 50% 75% max
COUNT 214282.0 1.000000 0.000000 1.0 1.0 1.0 1.0 1.0
SALARY 214282.0 66479.453855 39471.623281 3913.0 35830.0 54424.0 86910.0 393699.0
LOS 214282.0 11.708865 12.631714 0.0 1.3 6.2 20.4 71.5
CPU times: user 14 s, sys: 111 ms, total: 14.1 s
Wall time: 14.1 s
In [5]:
%%time

if os.path.isfile(PickleJarPath+"/EMPDataOrig4Q.pkl"):
    print("Found the File! Loading Pickle Now!")
    EMPDataOrig4Q = unpickleObject("EMPDataOrig4Q")
else:
    ## Load EMPData Files

    indexes = []
    EMPDataFiles = []
    EMPDataList = []
    EMPDataOrig = []

    for i,qtr in enumerate(["Q1", "Q2", "Q3", "Q4"]): 
        EMPDataFiles.append(glob.glob(os.path.join(dataEMPPath, qtr + "/*.txt")))

        for j in range(0,len(EMPDataFiles[i])):
            EMPDataFiles[i][j] = EMPDataFiles[i][j].replace("\\","/")

        EMPDataList.append([])

        for j,file in enumerate(EMPDataFiles[i]):
            EMPDataList[i].append(pd.read_csv(file, dtype = 'str'))
            if i == 0:
                display(EMPDataList[i][j].head())

        ## Load the FactData files into it's own object
        indexes.append([])
            ##[qtr][fileindex from EMPDataList]
        indexes[i]=[j for j,x in enumerate(EMPDataFiles[i]) if dataEMPPath + '/' + qtr + '/FACTDATA' in x]   

        EMPDataOrig.append([])

        EMPDataOrig[i] = pd.concat([EMPDataList[i][indexes[i][j]] for j in range(0,len(indexes[i]))]) 
        EMPDataOrig[i]["QTR"] = str(i+1)

            ## modify data type for numerics
        EMPDataOrig[i]["SALARY"] = EMPDataOrig[i]["SALARY"].str.replace(',', '').str.replace('$', '').str.replace(' ', '').apply(pd.to_numeric)
      
        ## Load Metadata
        ##AGYSUB - AGYTYP, AGY
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTagy.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'AGYSUB', how = 'left')

        ##AGELVL - AGELVLT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTagelvl.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'AGELVL', how = 'left')

        #LOSLVL - LOSLVLT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTloslvl.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'LOSLVL', how = 'left')
        EMPDataOrig[i]["LOS"] = EMPDataOrig[i]["LOS"].apply(pd.to_numeric)
        
        ##LOC - LocTypeT, LocT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTloc.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'LOC', how = 'left')
 
        ##OCC - OCCTYPT, OCCFAM
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTocc.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'OCC', how = 'left')

        ##PATCO - PATCOT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTpatco.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'PATCO', how = 'left')

        ##PPGRD - PayPlan, PPGroup, PPTYP
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTppgrd.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'PPGRD', how = 'left')

        ##SALLVL - SALLVLT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTsallvl.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'SALLVL', how = 'left')

        ##TOA - TOATYP
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTtoa.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'TOA', how = 'left')

        ##WORKSCH - WSTYPT
        ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTwrksch.txt']
        EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'WORKSCH', how = 'left')

        display(EMPDataOrig[i].head())

    EMPDataOrig4Q = pd.concat([EMPDataOrig[j] for j in range(0,len(EMPDataOrig))])
    print("Original EMP data size of: "+str(len(EMPDataOrig4Q)))
    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["LOCTYP"] != "1"]))+" Non-US observations.")
    
       ## Remove Non-US Data
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["LOCTYP"] == "1"]

    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["OCCTYP"] == "3"]))+" observations with no specified Occupation.")

       ## Remove Observations with no specified occupation
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["OCCTYP"] != "3"]

    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["SALLVL"] == "Z"]))+" observations with no specified Salary.")

       ## Remove Observations with no specified salary
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["SALLVL"] != "Z"]

    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["LOSLVL"] == "Z"]))+" observations with no specified Length of Service.")

       ## Remove Observations with no specified LOSLVL
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["LOSLVL"] != "Z"]

    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] == "A"]))+" observations of Age Level A.")

        ## Remove Observations from Age Level A (less than 20 years old)
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] != "A"]

    print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] == "Z"]))+" observations with no specified Age Level.")

        ## Remove Observations with no specified Age Level
    EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] != "Z"]

        ## Fix differences in spaces on WORKSCHT Column
    EMPDataOrig4Q["WORKSCHT"] = np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="F", 'Full-time Nonseasonal',
                                    np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="I", 'Intermittent Nonseasonal',
                                             np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="P", 'Part-time Nonseasonal',
                                                      np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="G", 'Full-time Seasonal',
                                                            np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="J", 'Intermittent Seasonal',
                                                                    np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="Q", 'Part-time Seasonal',
                                                                            np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="T", 'Part-time Job Sharer Seasonal',
                                                                                    np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="S", 'Part-time Job Sharer Nonseasonal',
                                                                                            np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="B", 'Full-time Nonseasonal Baylor Plan',
                                                                                                    'NO WORK SCHEDULE REPORTED' ### ELSE case represents Night
                                                                                                     )
                                                                                             )
                                                                                     )
                                                                             )
                                                                     )
                                                              )
                                                     )
                                            )
                                   )    

    pickleObject(EMPDataOrig4Q, "EMPDataOrig4Q")

print("New EMP data size of: "+str(len(EMPDataOrig4Q)))
Found the File! Loading Pickle Now!
New EMP data size of: 8008911
CPU times: user 9.24 s, sys: 1.78 s, total: 11 s
Wall time: 11 s
In [6]:
display(EMPDataOrig4Q.describe().transpose())
count mean std min 25% 50% 75% max
SALARY 8008911.0 80067.37279 37918.758366 15120.0 51437.0 74130.0 99957.0 401589.0
LOS 8008911.0 13.06029 10.446755 0.0 4.9 10.0 20.1 71.1
In [7]:
%matplotlib inline

#sns.boxplot(y = "SALARY", data = EMPDataOrig4Q)

With both our separation and non-separation data loaded, we calculate three new attributes through aggregation or calculation amongst various attributes.

1) SEP Count by Date & Occupation – total number of separations (of any type) for a given Date and Occupation;

2) SEP Count by Date & Location – total number of separations (of any type) for a given Date and Location;

3) Industry Average Salary – Average salary amongst non-separated employees, grouped by quarter, occupation, pay grade, and work schedule;

We proceed, by concatenating our Separation and Non-Separation observations, and merge these newly calculated attributes to the concatenated dataset.

In [8]:
%%time
%matplotlib inline

##Aggregate Number of Total Separations in current month for given Occ
AggSEPCount_EFDATE_OCC= pd.DataFrame({'SEPCount_EFDATE_OCC' : OPMDataMerged.groupby(["EFDATE", "OCC"]).size()}).reset_index()
display(AggSEPCount_EFDATE_OCC.head())


##Aggregate Number of Total Separations in current month for given LOC
AggSEPCount_EFDATE_LOC = pd.DataFrame({'SEPCount_EFDATE_LOC' : OPMDataMerged.groupby(["EFDATE", "LOC"]).size()}).reset_index()
display(AggSEPCount_EFDATE_LOC.head())

##Average Quarterly EMP Salary by occ 
AggIndAvgSalary = pd.DataFrame({'count' : EMPDataOrig4Q.groupby(["QTR", "OCC", "PPGRD", "WORKSCHT"]).size()}).reset_index()
AggIndAvgSalary2 = pd.DataFrame({'IndSalarySum' : EMPDataOrig4Q.groupby(["QTR", "OCC", "PPGRD", "WORKSCHT"])["SALARY"].sum()}).reset_index()
AggIndAvgSalary = AggIndAvgSalary.merge(AggIndAvgSalary2,on=["QTR", "OCC", "PPGRD", "WORKSCHT"])
AggIndAvgSalary["IndAvgSalary"] = AggIndAvgSalary["IndSalarySum"]/AggIndAvgSalary["count"]
del AggIndAvgSalary["count"]
del AggIndAvgSalary["IndSalarySum"]
display(AggIndAvgSalary.head())
EFDATE OCC SEPCount_EFDATE_OCC
0 201410 0006 20
1 201410 0007 89
2 201410 0017 1
3 201410 0018 33
4 201410 0019 1
EFDATE LOC SEPCount_EFDATE_LOC
0 201410 01 239
1 201410 02 261
2 201410 04 499
3 201410 05 132
4 201410 06 1926
QTR OCC PPGRD WORKSCHT IndAvgSalary
0 1 0006 ES-** Full-time Nonseasonal 161827.273973
1 1 0006 GL-09 Full-time Nonseasonal 63970.126984
2 1 0006 GS-09 Full-time Nonseasonal 56876.500000
3 1 0006 GS-11 Full-time Nonseasonal 72865.783673
4 1 0006 GS-12 Full-time Nonseasonal 85742.663717
CPU times: user 2.98 s, sys: 367 ms, total: 3.34 s
Wall time: 3.34 s
In [9]:
#Merge Two Datasets
### NS SEP code means NonSeparation
###add hardcoded null value columns where applicable
EMPDataOrig4Q["SEP"] = "NS"
EMPDataOrig4Q["GENDER"] = np.nan
EMPDataOrig4Q["COUNT"] = np.nan

OPMDataMerged["DATECODE"] = OPMDataMerged["EFDATE"]

OPMColList = ["AGYSUB", "SEP", "DATECODE",   "AGELVL", "GENDER", "GSEGRD", "LOSLVL", "LOC", "OCC", "PATCO", "PPGRD", "SALLVL", "TOA", "WORKSCH", "COUNT", "SALARY", "LOS", "AGYTYP", "AGYTYPT", "AGY", "AGYT", "AGYSUBT", "QTR", "AGELVLT", "LOSLVLT", "LOCTYP", "LOCTYPT", "LOCT", "OCCTYP", "OCCTYPT", "OCCFAM", "OCCFAMT", "OCCT", "PATCOT", "PPTYP", "PPTYPT", "PPGROUP", "PPGROUPT", "PAYPLAN", "PAYPLANT", "SALLVLT", "TOATYP", "TOATYPT", "TOAT", "WSTYP", "WSTYPT", "WORKSCHT"]
EMPColList = ["AGYSUB", "SEP", "DATECODE", "AGELVL", "GENDER", "GSEGRD", "LOSLVL", "LOC", "OCC", "PATCO", "PPGRD", "SALLVL", "TOA", "WORKSCH", "COUNT", "SALARY", "LOS", "AGYTYP", "AGYTYPT", "AGY", "AGYT", "AGYSUBT", "QTR", "AGELVLT", "LOSLVLT", "LOCTYP", "LOCTYPT", "LOCT", "OCCTYP", "OCCTYPT", "OCCFAM", "OCCFAMT", "OCCT", "PATCOT", "PPTYP", "PPTYPT", "PPGROUP", "PPGROUPT", "PAYPLAN", "PAYPLANT", "SALLVLT", "TOATYP", "TOATYPT", "TOAT", "WSTYP", "WSTYPT", "WORKSCHT"]

OPMDataMerged = pd.concat([OPMDataMerged[OPMColList], EMPDataOrig4Q[EMPColList]], ignore_index=True)
print("Total concatenated data size for SEP and non-SEP: "+str(len(OPMDataMerged)))

OPMDataMerged = OPMDataMerged.merge(AggSEPCount_EFDATE_OCC, left_on = ['DATECODE','OCC'], right_on = ['EFDATE','OCC'], how = 'left')
OPMDataMerged = OPMDataMerged.merge(AggSEPCount_EFDATE_LOC, left_on = ['DATECODE','LOC'], right_on = ['EFDATE','LOC'], how = 'left')
OPMDataMerged = OPMDataMerged.merge(AggIndAvgSalary, on = ['QTR','OCC', 'PPGRD', 'WORKSCHT'], how = 'left')
OPMDataMerged["SalaryOverUnderIndAvg"] = OPMDataMerged["SALARY"] - OPMDataMerged["IndAvgSalary"]

del OPMDataMerged["EFDATE_x"]
del OPMDataMerged["EFDATE_y"]

display(OPMDataMerged.head())
display(OPMDataMerged.tail())
Total concatenated data size for SEP and non-SEP: 8223193
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg
0 AA00 SC 201507 C M 11 A 11 0905 1 GS-11 F 40 F 1.0 63722.0 0.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 4 25-29 Less than 1 year 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 2 Non-permanent 40-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 205.0 1319 64540.593830 -818.593830
1 AA00 SC 201506 D F 15 C 11 0905 1 GS-15 L 30 F 1.0 126245.0 4.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 3 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $120,000 - $129,999 1 Permanent 30-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 207.0 1132 149864.298504 -23619.298504
2 AF** SA 201503 H M 11 C 48 2210 2 GS-11 F 10 F 1.0 66585.0 4.9 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF**-INVALID 2 50-54 3 - 4 years 1 United States 48-TEXAS 1 White Collar 22 22xx-INFORMATION TECHNOLOGY 2210-INFORMATION TECHNOLOGY MANAGEMENT Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 439.0 1087 71530.963755 -4945.963755
3 AF02 SD 201506 I M 15 J 35 0301 2 GS-15 O 10 F 1.0 156737.0 39.8 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF02-AIR FORCE INSPECTION AGENCY (FO) 3 55-59 35 years or more 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $150,000 - $159,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 670.0 265 146735.220304 10001.779696
4 AF03 SC 201509 H M 13 B 06 0301 2 GS-13 I 15 F 1.0 92973.0 1.0 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... 4 50-54 1 - 2 years 1 United States 06-CALIFORNIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $90,000 - $99,999 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 721.0 1853 101641.124025 -8668.124025
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg
8223188 ZU00 NS 201509 D NaN NaN C 11 0301 2 AD-00 G 48 F NaN 76377.0 4.8 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $70,000 - $79,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -39463.182250
8223189 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 M 48 F NaN 139517.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $130,000 - $139,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 23676.817750
8223190 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 O 48 F NaN 158671.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $150,000 - $159,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 42830.817750
8223191 ZU00 NS 201509 B NaN NaN B 11 0301 2 AD-00 C 48 F NaN 36244.0 1.6 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 20-24 1 - 2 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $30,000 - $39,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -79596.182250
8223192 ZU00 NS 201509 E NaN NaN D 11 0505 2 AD-00 I 48 F NaN 99288.0 5.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 35-39 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 05 05xx-ACCOUNTING AND BUDGET 0505-FINANCIAL MANAGEMENT Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $90,000 - $99,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 7.0 1391 148382.833333 -49094.833333
In [10]:
print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()]))

display(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()][["SEP","DATECODE", "OCC"]].drop_duplicates())
50993
SEP DATECODE OCC
217479 NS 201412 7402
217582 NS 201412 7420
217603 NS 201412 1051
217663 NS 201412 1054
218685 NS 201412 2504
218871 NS 201412 8201
218999 NS 201412 4104
219003 NS 201412 4715
219135 NS 201412 0698
220085 NS 201412 0019
220426 NS 201412 3602
221497 NS 201412 2608
221637 NS 201412 3725
224242 NS 201412 6968
225410 NS 201412 0392
226132 NS 201412 3606
228440 NS 201412 2601
231003 NS 201412 3940
231189 NS 201412 5439
246316 NS 201412 1725
246379 NS 201412 5317
246874 NS 201412 5737
247551 NS 201412 1386
254687 NS 201412 0394
259606 NS 201412 4819
264047 NS 201412 2144
266228 NS 201412 1056
268830 NS 201412 5736
270371 NS 201412 0021
271810 NS 201412 3872
271986 NS 201412 4301
273244 NS 201412 3701
273326 NS 201412 6656
273665 NS 201412 8601
275118 NS 201412 3858
275185 NS 201412 4745
277206 NS 201412 4816
279195 NS 201412 1699
284633 NS 201412 5423
289472 NS 201412 1321
295232 NS 201412 3727
305466 NS 201412 1521
319867 NS 201412 0642
325062 NS 201412 4373
332634 NS 201412 2110
349140 NS 201412 0134
376747 NS 201412 0435
377161 NS 201412 1382
380400 NS 201412 0440
380444 NS 201412 0890
380485 NS 201412 1221
380534 NS 201412 0799
380549 NS 201412 0471
381610 NS 201412 5002
381660 NS 201412 0302
382823 NS 201412 4737
383757 NS 201412 1384
387315 NS 201412 3511
395468 NS 201412 1380
407172 NS 201412 0880
417564 NS 201412 1202
422349 NS 201412 0184
431266 NS 201412 5729
444970 NS 201412 3515
455591 NS 201412 4414
456269 NS 201412 1850
457198 NS 201412 0160
461672 NS 201412 0136
475474 NS 201412 1374
475784 NS 201412 6517
475903 NS 201412 6605
480003 NS 201412 5310
485323 NS 201412 3605
500481 NS 201412 4741
503007 NS 201412 1397
505340 NS 201412 3314
506488 NS 201412 5323
516998 NS 201412 4101
519478 NS 201412 0322
558933 NS 201412 4010
559676 NS 201412 0648
593579 NS 201412 3301
596650 NS 201412 3101
625941 NS 201412 7603
661444 NS 201412 4807
661550 NS 201412 3428
662101 NS 201412 5738
676144 NS 201412 5205
685955 NS 201412 6505
686300 NS 201412 3546
686445 NS 201412 5427
704369 NS 201412 2161
709277 NS 201412 9927
709522 NS 201412 9968
711524 NS 201412 9944
711608 NS 201412 9916
711636 NS 201412 9957
712047 NS 201412 9960
712126 NS 201412 9971
722081 NS 201412 1226
722670 NS 201412 1223
725221 NS 201412 1299
766570 NS 201412 1999
859013 NS 201412 1541
966674 NS 201412 0106
966676 NS 201412 0243
966716 NS 201412 0140
971520 NS 201412 0357
1087460 NS 201412 1046
1145227 NS 201412 4717
1345880 NS 201412 2501
1359410 NS 201412 3910
1363515 NS 201412 9961
1384266 NS 201412 9905
1389961 NS 201412 5419
1502113 NS 201412 3604
1503521 NS 201412 3808
1528597 NS 201412 1021
1534692 NS 201412 9942
1534710 NS 201412 9997
1534741 NS 201412 9975
1534742 NS 201412 9930
1534756 NS 201412 9945
1534808 NS 201412 9972
1534811 NS 201412 9995
1534822 NS 201412 9940
1534917 NS 201412 9993
1534931 NS 201412 9982
1535050 NS 201412 9999
1535060 NS 201412 9915
1535179 NS 201412 9955
1535640 NS 201412 9919
1535643 NS 201412 9918
1536227 NS 201412 9914
1537487 NS 201412 9921
1538507 NS 201412 9903
1562285 NS 201412 5221
1620441 NS 201412 1831
1846895 NS 201412 5440
1846946 NS 201412 3513
1848770 NS 201412 4406
1848778 NS 201412 4454
1872827 NS 201412 0593
1906935 NS 201412 0625
1937597 NS 201412 0637
2209093 NS 201503 1054
2209135 NS 201503 0050
2209165 NS 201503 7420
2209549 NS 201503 4805
2209562 NS 201503 7401
2209567 NS 201503 0062
2209830 NS 201503 1051
2210138 NS 201503 5767
2210218 NS 201503 2504
2210251 NS 201503 3940
2210298 NS 201503 4715
2210777 NS 201503 0017
2210790 NS 201503 0019
2210923 NS 201503 5026
2211013 NS 201503 3602
2211337 NS 201503 4255
2211397 NS 201503 3606
2211430 NS 201503 3809
2211601 NS 201503 1501
2211622 NS 201503 2608
2211840 NS 201503 3901
2211850 NS 201503 0698
2212525 NS 201503 0667
2213232 NS 201503 8610
2216698 NS 201503 4605
2216720 NS 201503 5439
2217256 NS 201503 1015
2221243 NS 201503 3725
2237081 NS 201503 5737
2237389 NS 201503 5317
2237807 NS 201503 1725
2237838 NS 201503 0131
2238836 NS 201503 1386
2239148 NS 201503 4417
2239413 NS 201503 4401
2245721 NS 201503 5876
2247270 NS 201503 4201
2250530 NS 201503 2135
2251137 NS 201503 0394
2254034 NS 201503 4819
2255121 NS 201503 7001
2255147 NS 201503 0021
2255494 NS 201503 2144
2257059 NS 201503 4602
2257560 NS 201503 4601
2258388 NS 201503 1056
2262895 NS 201503 3769
2262896 NS 201503 3707
2262911 NS 201503 4850
2263867 NS 201503 6656
2264417 NS 201503 4745
2264609 NS 201503 3872
2264927 NS 201503 4616
2265294 NS 201503 4301
2266492 NS 201503 8601
2267324 NS 201503 3727
2268045 NS 201503 7006
2269170 NS 201503 3712
2269386 NS 201503 2032
2273426 NS 201503 1521
2273438 NS 201503 0688
2274894 NS 201503 3858
2278199 NS 201503 4373
2282112 NS 201503 3401
2290158 NS 201503 4816
2305124 NS 201503 1321
2312141 NS 201503 5313
2319977 NS 201503 0642
2322021 NS 201503 1372
2326923 NS 201503 2110
2332047 NS 201503 1815
2367236 NS 201503 1146
2367963 NS 201503 1382
2368576 NS 201503 0435
2371118 NS 201503 0471
2371219 NS 201503 0487
2371445 NS 201503 1221
2373151 NS 201503 5002
2373803 NS 201503 0799
2373812 NS 201503 1384
2373939 NS 201503 0302
2375066 NS 201503 5001
2382890 NS 201503 0135
2383750 NS 201503 1380
2394962 NS 201503 5786
2398815 NS 201503 1202
2421724 NS 201503 5729
2422326 NS 201503 0309
2429714 NS 201503 3511
2435949 NS 201503 3515
2445822 NS 201503 4414
2445959 NS 201503 4402
2446076 NS 201503 1850
2446395 NS 201503 0160
2451711 NS 201503 0136
2465677 NS 201503 6517
2465911 NS 201503 1374
2469001 NS 201503 7601
2469608 NS 201503 5310
2473512 NS 201503 3605
2478500 NS 201503 4741
2481863 NS 201503 1630
2487747 NS 201503 5042
2492992 NS 201503 1397
2493609 NS 201503 5318
... ... ... ...
4493254 NS 201506 3605
4500627 NS 201506 5784
4501690 NS 201506 5323
4505453 NS 201506 3314
4511881 NS 201506 0322
4567936 NS 201506 0313
4590882 NS 201506 4754
4591246 NS 201506 3101
4618223 NS 201506 3301
4625645 NS 201506 7603
4647430 NS 201506 3106
4648437 NS 201506 4101
4656879 NS 201506 0873
4659315 NS 201506 5738
4660107 NS 201506 3802
4666481 NS 201506 4807
4673196 NS 201506 5205
4683095 NS 201506 6505
4683209 NS 201506 3546
4683500 NS 201506 5427
4706313 NS 201506 9924
4706406 NS 201506 9954
4706448 NS 201506 9923
4706828 NS 201506 9932
4706944 NS 201506 9920
4706992 NS 201506 9916
4707380 NS 201506 9971
4707627 NS 201506 9960
4711283 NS 201506 9944
4718825 NS 201506 1226
4719354 NS 201506 1223
4720834 NS 201506 1299
4838452 NS 201506 1163
4941776 NS 201506 0082
4967369 NS 201506 0140
4967373 NS 201506 0243
4967385 NS 201506 0106
4971515 NS 201506 0357
5079100 NS 201506 1046
5238592 NS 201506 1889
5364837 NS 201506 9905
5366360 NS 201506 3910
5379475 NS 201506 9961
5397362 NS 201506 4416
5397878 NS 201506 5419
5515876 NS 201506 3808
5525861 NS 201506 1021
5543637 NS 201506 9982
5543654 NS 201506 9942
5543662 NS 201506 9997
5543680 NS 201506 9955
5543695 NS 201506 9975
5543698 NS 201506 9906
5543700 NS 201506 9991
5543706 NS 201506 9908
5543725 NS 201506 9930
5543741 NS 201506 9976
5543817 NS 201506 9919
5543902 NS 201506 9999
5543929 NS 201506 9929
5544204 NS 201506 9940
5544280 NS 201506 9939
5544281 NS 201506 9914
5544294 NS 201506 9918
5544388 NS 201506 9915
5545181 NS 201506 9921
5560038 NS 201506 9904
5570248 NS 201506 5221
5612184 NS 201506 3428
5631455 NS 201506 1831
5708205 NS 201506 2125
5846894 NS 201506 5440
5847347 NS 201506 3513
5848754 NS 201506 4406
5848756 NS 201506 4441
5848778 NS 201506 4454
5848780 NS 201506 4449
5874147 NS 201506 0593
5899552 NS 201506 0625
5915763 NS 201506 0637
6215137 NS 201509 7420
6215177 NS 201509 1054
6215183 NS 201509 0062
6215311 NS 201509 1051
6216048 NS 201509 0319
6216143 NS 201509 0332
6216452 NS 201509 5026
6216656 NS 201509 3602
6216697 NS 201509 4255
6216714 NS 201509 3610
6216843 NS 201509 4715
6217038 NS 201509 8610
6217069 NS 201509 1501
6217109 NS 201509 4714
6217177 NS 201509 8201
6218219 NS 201509 0017
6221598 NS 201509 5439
6221611 NS 201509 6511
6222813 NS 201509 3901
6223238 NS 201509 6968
6224360 NS 201509 3725
6242835 NS 201509 5317
6242837 NS 201509 7305
6243763 NS 201509 3111
6244182 NS 201509 1725
6244772 NS 201509 3606
6245272 NS 201509 4401
6245800 NS 201509 1386
6246495 NS 201509 1815
6248929 NS 201509 1361
6251962 NS 201509 4201
6252800 NS 201509 5401
6253625 NS 201509 5737
6260660 NS 201509 4819
6261977 NS 201509 7001
6262910 NS 201509 2144
6265927 NS 201509 0021
6266002 NS 201509 4602
6269692 NS 201509 0967
6269891 NS 201509 4840
6270010 NS 201509 3707
6270144 NS 201509 5423
6270200 NS 201509 6656
6270425 NS 201509 3872
6270432 NS 201509 4745
6270882 NS 201509 4361
6271499 NS 201509 3701
6271656 NS 201509 3727
6272327 NS 201509 4816
6272630 NS 201509 4850
6273748 NS 201509 3858
6274513 NS 201509 1222
6276289 NS 201509 4616
6276382 NS 201509 4301
6277379 NS 201509 7006
6280721 NS 201509 8601
6308684 NS 201509 1321
6325366 NS 201509 1056
6327220 NS 201509 1521
6334577 NS 201509 2110
6346443 NS 201509 4417
6377693 NS 201509 0434
6378110 NS 201509 1999
6378827 NS 201509 0440
6378853 NS 201509 0487
6378954 NS 201509 0437
6379113 NS 201509 5002
6379245 NS 201509 1384
6380466 NS 201509 0410
6381133 NS 201509 0308
6381165 NS 201509 0302
6391135 NS 201509 0135
6392086 NS 201509 1380
6392140 NS 201509 0965
6405195 NS 201509 0880
6428400 NS 201509 1202
6433233 NS 201509 5729
6435611 NS 201509 0309
6438731 NS 201509 0184
6453143 NS 201509 3515
6464352 NS 201509 4414
6464503 NS 201509 1850
6470218 NS 201509 0136
6483824 NS 201509 1374
6485608 NS 201509 2501
6488173 NS 201509 1630
6490604 NS 201509 5310
6490614 NS 201509 4741
6509539 NS 201509 3605
6511085 NS 201509 0072
6511515 NS 201509 1397
6512110 NS 201509 5782
6512216 NS 201509 5323
6516672 NS 201509 3314
6584508 NS 201509 0635
6593181 NS 201509 0313
6606313 NS 201509 3101
6628632 NS 201509 3301
6639321 NS 201509 7603
6651971 NS 201509 1046
6658080 NS 201509 3106
6658384 NS 201509 4101
6666886 NS 201509 0873
6667185 NS 201509 4373
6669581 NS 201509 4807
6670232 NS 201509 5738
6672326 NS 201509 3802
6693020 NS 201509 5427
6711618 NS 201509 2161
6713304 NS 201509 0958
6716480 NS 201509 9973
6716659 NS 201509 9916
6716801 NS 201509 9932
6717186 NS 201509 9971
6718325 NS 201509 9965
6719926 NS 201509 9960
6729094 NS 201509 1226
6729105 NS 201509 1223
6731464 NS 201509 1299
6741881 NS 201509 5313
6803145 NS 201509 1730
6820061 NS 201509 6941
6847199 NS 201509 1163
6868697 NS 201509 1541
6976225 NS 201509 0140
6976233 NS 201509 0106
6976245 NS 201509 0243
6980096 NS 201509 0357
7155776 NS 201509 4717
7247692 NS 201509 1881
7371930 NS 201509 3910
7376502 NS 201509 9961
7392449 NS 201509 0485
7404982 NS 201509 4403
7405044 NS 201509 4416
7405049 NS 201509 5419
7521217 NS 201509 3808
7524148 NS 201509 3604
7536239 NS 201509 1021
7552743 NS 201509 9991
7552774 NS 201509 9975
7552775 NS 201509 9998
7552813 NS 201509 9976
7552825 NS 201509 9994
7552870 NS 201509 9988
7552961 NS 201509 9940
7552962 NS 201509 9982
7553012 NS 201509 9915
7553021 NS 201509 9930
7553042 NS 201509 9929
7553061 NS 201509 9999
7553126 NS 201509 9993
7553127 NS 201509 9908
7553159 NS 201509 9955
7553740 NS 201509 9914
7553846 NS 201509 9939
7554073 NS 201509 9921
7554679 NS 201509 9919
7554855 NS 201509 9918
7569222 NS 201509 9904
7579652 NS 201509 5221
7612169 NS 201509 3428
7717999 NS 201509 2125
7851216 NS 201509 5440
7851838 NS 201509 3513
7853201 NS 201509 4406
7853227 NS 201509 4454
7884942 NS 201509 0593
7965633 NS 201509 0625
8001140 NS 201509 0637

660 rows × 3 columns

These 50993 Non-Separation observations do not have coverage within the Separation Dataset, thus, we will remove these observations as out of scope demographic in our analysis. Any attempt in predicting these values will not have enough data to support a significant response.

In [11]:
OPMDataMerged = OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].notnull()]

print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()]))

print(len(OPMDataMerged))
0
8172200
In [12]:
print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_LOC"].isnull()]))

display(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_LOC"].isnull()][["SEP","DATECODE","LOC"]].drop_duplicates())
0
SEP DATECODE LOC
In [13]:
print(len(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()]))

display(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()][["QTR", "SEP","OCCT", "PPGRD", "WORKSCHT"]].drop_duplicates())
1293
QTR SEP OCCT PPGRD WORKSCHT
257 4 SC 7401-MISC FOOD PREPARATION AND SERVING WG-01 Full-time Nonseasonal
627 4 SC 1301-GENERAL PHYSICAL SCIENCE AD-24 Part-time Nonseasonal
697 4 SJ 0199-SOCIAL SCIENCE STUDENT TRAINEE GS-02 Intermittent Nonseasonal
749 4 SC 3940-BROADCASTING EQUIPMENT OPERATING WG-10 Full-time Nonseasonal
2401 4 SJ 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GS-02 Intermittent Seasonal
3412 2 SC 5003-GARDENING WG-04 Full-time Seasonal
3471 1 SA 5003-GARDENING WG-04 Full-time Seasonal
3551 3 SD 5716-ENGINEERING EQUIPMENT OPERATING WS-14 Full-time Nonseasonal
4937 3 SC 0819-ENVIRONMENTAL ENGINEERING GS-11 Part-time Job Sharer Nonseasonal
5285 1 SD 5716-ENGINEERING EQUIPMENT OPERATING WG-08 Intermittent Seasonal
5363 4 SJ 0189-RECREATION AID AND ASSISTANT GS-03 Intermittent Nonseasonal
5763 1 SD 2005-SUPPLY CLERICAL AND TECHNICIAN GS-04 Part-time Job Sharer Nonseasonal
6079 3 SC 0180-PSYCHOLOGY NH-02 Full-time Nonseasonal
6957 3 SD 0810-CIVIL ENGINEERING DR-03 Full-time Nonseasonal
7015 1 SA 1306-HEALTH PHYSICS DR-01 Full-time Nonseasonal
7376 4 SC 1699-EQUIPMENT AND FACILITIES MANAGEMENT STUDE... GS-05 Full-time Nonseasonal
7395 3 SC 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE DU-01 Full-time Nonseasonal
7464 3 SD 3769-SHOT PEENING MACHINE OPERATING WS-07 Full-time Nonseasonal
7512 4 SC 0840-NUCLEAR ENGINEERING DR-03 Full-time Nonseasonal
7675 2 SD 4714-MODEL MAKING WL-15 Full-time Nonseasonal
7727 4 SC 0189-RECREATION AID AND ASSISTANT GS-02 Part-time Seasonal
7877 4 SJ 0189-RECREATION AID AND ASSISTANT GS-02 Part-time Seasonal
8054 4 SC 0665-SPEECH PATHOLOGY AND AUDIOLOGY DR-03 Full-time Nonseasonal
8160 4 SC 4102-PAINTING WG-05 Part-time Seasonal
8216 2 SA 5725-CRANE OPERATING WG-08 Full-time Nonseasonal
8320 4 SD 5401-MISC INDUSTRIAL EQUIPMENT OPERATION WS-11 Full-time Nonseasonal
8325 1 SC 0189-RECREATION AID AND ASSISTANT DU-01 Part-time Nonseasonal
8389 4 SD 3705-NON-DESTRUCTIVE TESTING WS-16 Full-time Nonseasonal
8435 4 SD 1330-ASTRONOMY AND SPACE SCIENCE DR-04 Full-time Nonseasonal
8449 1 SK 4102-PAINTING WG-05 Part-time Seasonal
9741 4 SC 0189-RECREATION AID AND ASSISTANT GS-03 Part-time Seasonal
9890 1 SI 8801-MISCELLANEOUS AIRCRAFT OVERHAUL WG-08 Part-time Nonseasonal
9903 1 SD 0610-NURSE DR-01 Full-time Nonseasonal
9916 2 SD 0130-FOREIGN AFFAIRS DO-02 Full-time Nonseasonal
10139 4 SC 6901-MISC WAREHOUSING AND STOCK HANDLING WG-06 Part-time Job Sharer Nonseasonal
10656 2 SC 5309-HEATING & BOILER PLANT EQUIPMT MECHANIC WL-11 Full-time Nonseasonal
11491 4 SC 1008-INTERIOR DESIGN GG-12 Full-time Nonseasonal
11516 4 SI 0854-COMPUTER ENGINEERING GG-11 Full-time Nonseasonal
11941 1 SJ 0201-HUMAN RESOURCES MANAGEMENT GS-06 Full-time Nonseasonal
12265 4 SJ 0335-COMPUTER CLERK AND ASSISTANT GS-09 Part-time Nonseasonal
12541 2 SJ 6501-MISC AMMUN, EXPLOSIVES, & TOXIC MATER WORK WG-12 Full-time Nonseasonal
13028 3 SJ 5378-POWERED SUPPORT SYSTEMS MECHANIC WG-10 Part-time Nonseasonal
13029 2 SJ 2610-ELECTRONIC INTEGRATED SYSTEMS MECHANIC WG-12 Part-time Nonseasonal
13643 3 SJ 8602-AIRCRAFT ENGINE MECHANIC WG-04 Full-time Nonseasonal
14084 3 SJ 0340-PROGRAM MANAGEMENT GS-14 Part-time Nonseasonal
15523 2 SJ 2892-AIRCRAFT ELECTRICIAN WG-06 Full-time Nonseasonal
16075 3 SJ 2892-AIRCRAFT ELECTRICIAN WG-07 Full-time Nonseasonal
16454 2 SC 5378-POWERED SUPPORT SYSTEMS MECHANIC WG-06 Full-time Nonseasonal
16512 1 SJ 8602-AIRCRAFT ENGINE MECHANIC WG-06 Full-time Nonseasonal
16691 2 SJ 0132-INTELLIGENCE GS-04 Full-time Nonseasonal
17344 1 SJ 2101-TRANSPORTATION SPECIALIST GS-07 Intermittent Nonseasonal
17376 4 SJ 0335-COMPUTER CLERK AND ASSISTANT GS-07 Part-time Nonseasonal
17426 3 SC 0335-COMPUTER CLERK AND ASSISTANT GS-06 Intermittent Nonseasonal
17464 3 SJ 8852-AIRCRAFT MECHANIC WG-04 Full-time Nonseasonal
17763 1 SJ 4818-AIRCRAFT SURVIVAL FLIGHT EQUIPMENT REPAIR WG-10 Part-time Nonseasonal
19309 4 SD 0701-VETERINARY MEDICAL SCIENCE GM-15 Full-time Nonseasonal
19312 3 SD 0410-ZOOLOGY ST-00 Full-time Nonseasonal
19704 2 SJ 3511-LABORATORY WORKING WG-01 Part-time Nonseasonal
19768 2 SD 0435-PLANT PHYSIOLOGY GM-15 Full-time Nonseasonal
20138 3 SC 0802-ENGINEERING TECHNICAL GS-03 Part-time Nonseasonal
20285 4 SC 3566-CUSTODIAL WORKING WG-01 Intermittent Seasonal
20720 2 SC 0135-FOREIGN AGRICULTURAL AFFAIRS FP-03 Full-time Nonseasonal
20754 4 SJ 0119-ECONOMICS ASSISTANT GS-03 Full-time Seasonal
20760 4 SJ 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GS-04 Full-time Seasonal
20777 3 SD 0135-FOREIGN AGRICULTURAL AFFAIRS FE-01 Full-time Nonseasonal
20878 1 SJ 0189-RECREATION AID AND ASSISTANT GS-01 Full-time Nonseasonal
20928 1 SJ 0462-FORESTRY TECHNICIAN GS-04 Intermittent Seasonal
21599 3 SJ 0455-RANGE TECHNICIAN GS-05 Part-time Nonseasonal
23681 1 SJ 0102-SOCIAL SCIENCE AID AND TECHNICIAN GS-03 Full-time Nonseasonal
24266 1 SI 8610-SMALL ENGINE MECHANIC WG-06 Full-time Seasonal
24310 3 SJ 0455-RANGE TECHNICIAN GS-06 Intermittent Nonseasonal
26446 1 SC 0304-INFORMATION RECEPTIONIST GS-04 Intermittent Nonseasonal
27067 1 SJ 0455-RANGE TECHNICIAN GS-05 Intermittent Seasonal
29585 2 SD 1071-AUDIOVISUAL PRODUCTION GM-13 Full-time Nonseasonal
29689 2 SJ 0802-ENGINEERING TECHNICAL GS-06 Part-time Seasonal
29724 2 SJ 0462-FORESTRY TECHNICIAN GS-01 Full-time Nonseasonal
29878 1 SJ 1001-GENERAL ARTS AND INFORMATION GS-05 Intermittent Seasonal
30106 1 SJ 5201-MISCELLANEOUS OCCUPATIONS WG-05 Intermittent Nonseasonal
30137 3 SA 0430-BOTANY GS-07 Full-time Nonseasonal
30156 2 SJ 0102-SOCIAL SCIENCE AID AND TECHNICIAN GS-04 Intermittent Nonseasonal
30803 4 SC 0189-RECREATION AID AND ASSISTANT GS-04 Intermittent Nonseasonal
30817 3 SJ 1341-METEOROLOGICAL TECHNICIAN GS-08 Part-time Nonseasonal
32058 1 SJ 1371-CARTOGRAPHIC TECHNICIAN GS-07 Intermittent Nonseasonal
32562 1 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GS-02 Full-time Seasonal
33011 1 SC 0335-COMPUTER CLERK AND ASSISTANT GS-07 Part-time Nonseasonal
33713 3 SD 4715-EXHIBITS MAKING/MODELING WL-07 Full-time Nonseasonal
33737 4 SD 0850-ELECTRICAL ENGINEERING GM-14 Full-time Nonseasonal
33870 4 SJ 0318-SECRETARY GS-03 Intermittent Nonseasonal
34226 1 SC 0322-CLERK-TYPIST GS-04 Intermittent Nonseasonal
35280 3 SJ 0486-WILDLIFE BIOLOGY AD-00 Intermittent Nonseasonal
35308 3 SJ 1421-ARCHIVES TECHNICIAN GS-07 Full-time Seasonal
35369 2 SJ 0326-OFFICE AUTOMATION CLERICAL AND ASSISTANCE GS-04 Intermittent Seasonal
35683 3 SC 0421-PLANT PROTECTION TECHNICIAN GS-05 Intermittent Nonseasonal
35733 4 SJ 1421-ARCHIVES TECHNICIAN GS-07 Intermittent Nonseasonal
35779 4 SJ 0404-BIOLOGICAL SCIENCE TECHNICIAN AD-00 Part-time Seasonal
36150 3 SC 1863-FOOD INSPECTION GS-08 Intermittent Nonseasonal
36341 4 SC 1899-INVESTIGATION STUDENT TRAINEE GS-03 Full-time Nonseasonal
36424 2 SD 0896-INDUSTRIAL ENGINEERING GM-13 Full-time Nonseasonal
36788 2 SD 0935-ADMINISTRATIVE LAW JUDGE AL-02 Full-time Nonseasonal
37280 1 SG 0905-GENERAL ATTORNEY FE-02 Full-time Nonseasonal
37464 3 SJ 1140-TRADE SPECIALIST GS-15 Intermittent Nonseasonal
37478 4 SG 0130-FOREIGN AFFAIRS FE-03 Full-time Nonseasonal
37721 1 SC 0809-CONSTRUCTION CONTROL TECHNICAL NJ-03 Full-time Nonseasonal
37845 4 SC 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM NH-02 Part-time Nonseasonal
38023 1 SC 0318-SECRETARY NK-02 Part-time Nonseasonal
38509 1 SC 0184-SOCIOLOGY GG-12 Full-time Nonseasonal
38675 2 SD 0896-INDUSTRIAL ENGINEERING GG-13 Full-time Nonseasonal
39900 3 SJ 5803-HEAVY MOBILE EQUIPMENT MECHANIC WG-08 Intermittent Nonseasonal
40629 4 SC 6904-TOOLS AND PARTS ATTENDING WG-02 Full-time Nonseasonal
41443 4 SD 5407-ELECTRICAL POWER CONTROLLING WG-08 Full-time Nonseasonal
41565 2 SJ 0085-SECURITY GUARD GS-06 Part-time Nonseasonal
41658 1 SJ 5705-TRACTOR OPERATING WL-04 Intermittent Nonseasonal
41759 1 SD 6610-SMALL ARMS REPAIRING WL-09 Full-time Nonseasonal
41774 1 SI 0072-FINGERPRINT IDENTIFICATION GS-09 Full-time Nonseasonal
41961 1 SC 0085-SECURITY GUARD GS-03 Intermittent Nonseasonal
42017 3 SJ 5716-ENGINEERING EQUIPMENT OPERATING WL-08 Intermittent Nonseasonal
42082 1 SJ 5784-RIVERBOAT OPERATING XH-14 Full-time Seasonal
42267 1 SC 0802-ENGINEERING TECHNICAL GS-10 Intermittent Nonseasonal
42308 3 SJ 6907-MATERIALS HANDLER WG-06 Intermittent Nonseasonal
42324 1 SJ 5786-SMALL CRAFT OPERATING WG-08 Intermittent Nonseasonal
42328 1 SC 0856-ELECTRONICS TECHNICAL GS-09 Intermittent Nonseasonal
42414 2 SC 1699-EQUIPMENT AND FACILITIES MANAGEMENT STUDE... GS-03 Full-time Nonseasonal
42653 4 SC 2805-ELECTRICIAN WY-10 Intermittent Nonseasonal
42718 4 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE DE-02 Part-time Nonseasonal
42750 4 SJ 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER XF-01 Full-time Nonseasonal
42799 4 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE DE-01 Intermittent Nonseasonal
42910 3 SC 0499-BIOLOGICAL SCIENCE STUDENT TRAINEE DB-01 Full-time Nonseasonal
43219 3 SJ 5407-ELECTRICAL POWER CONTROLLING WB-00 Part-time Nonseasonal
43234 3 SJ 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER WG-03 Part-time Nonseasonal
43355 3 SJ 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM DJ-05 Intermittent Nonseasonal
43595 1 SJ 5426-LOCK AND DAM OPERATING WY-03 Full-time Nonseasonal
43739 2 SF 7404-COOKING XH-06 Full-time Seasonal
44192 3 SJ 1599-MATHEMATICS AND STATISTICS STUDENT TRAINEE DB-01 Part-time Nonseasonal
44297 4 SD 1530-STATISTICS DB-04 Part-time Nonseasonal
44406 2 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GS-05 Intermittent Nonseasonal
44419 2 SA 0890-AGRICULTURAL ENGINEERING DB-02 Full-time Nonseasonal
44469 2 SK 7401-MISC FOOD PREPARATION AND SERVING XH-07 Full-time Seasonal
44784 2 SI 5782-SHIP OPERATING XH-13 Full-time Seasonal
44788 1 SJ 7404-COOKING XF-05 Full-time Nonseasonal
44901 2 SC 1640-FACILITY OPERATIONS SERVICES GS-09 Intermittent Nonseasonal
44952 2 SC 5782-SHIP OPERATING XH-13 Full-time Seasonal
45157 3 SD 0808-ARCHITECTURE DB-05 Full-time Nonseasonal
45354 2 SD 5725-CRANE OPERATING XH-12 Full-time Nonseasonal
45458 1 SJ 3703-WELDING WG-10 Intermittent Nonseasonal
45537 1 SK 0544-CIVILIAN PAY GS-06 Full-time Seasonal
50004 3 SC 0682-DENTAL HYGIENE GS-07 Intermittent Nonseasonal
50228 1 SJ 0560-BUDGET ANALYSIS DJ-03 Part-time Nonseasonal
50820 3 SC 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... DB-02 Part-time Nonseasonal
51222 3 SC 0186-SOCIAL SERVICES AID AND ASSISTANT GS-08 Part-time Nonseasonal
51430 1 SC 1712-TRAINING INSTRUCTION DJ-03 Full-time Nonseasonal
51929 1 SC 0089-EMERGENCY MANAGEMENT SPECIALIST DJ-04 Full-time Nonseasonal
52912 2 SC 0085-SECURITY GUARD GS-12 Full-time Nonseasonal
53771 3 SC 6610-SMALL ARMS REPAIRING WG-07 Full-time Nonseasonal
54340 1 SJ 1035-PUBLIC AFFAIRS GS-06 Full-time Nonseasonal
55115 3 SJ 5801-MISC TRANSPORTATION/MOBILE EQUIPMT MAINTNE WG-08 Intermittent Nonseasonal
55382 4 SC 8810-AIRCRAFT PROPELLER MECHANIC WG-08 Full-time Nonseasonal
55575 4 SC 0203-HUMAN RESOURCES ASSISTANCE GS-05 Intermittent Nonseasonal
55689 4 SJ 2604-ELECTRONICS MECHANIC WG-12 Part-time Nonseasonal
57336 3 SJ 5413-FUEL DISTRIBUTION SYSTEM OPERATING WG-05 Intermittent Nonseasonal
57671 1 SJ 5801-MISC TRANSPORTATION/MOBILE EQUIPMT MAINTNE WG-08 Intermittent Nonseasonal
59155 3 SD 3101-MISC FABRIC AND LEATHER WORK WS-11 Full-time Nonseasonal
59259 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... AD-00 Full-time Nonseasonal
60044 3 SA 6610-SMALL ARMS REPAIRING WL-06 Full-time Nonseasonal
60716 4 SJ 0101-SOCIAL SCIENCE AD-00 Part-time Nonseasonal
61107 2 SD 5201-MISCELLANEOUS OCCUPATIONS WS-15 Full-time Nonseasonal
61239 2 SI 2601-MISC ELECTRONIC EQUIPMT INSTALL & MAINTNE WG-04 Full-time Nonseasonal
62578 4 SA 2608-ELECTRONIC DIGITAL COMPUTER MECHANIC WL-10 Full-time Nonseasonal
63872 3 SF 2005-SUPPLY CLERICAL AND TECHNICIAN GS-04 Full-time Seasonal
63902 3 SF 2610-ELECTRONIC INTEGRATED SYSTEMS MECHANIC WT-00 Full-time Nonseasonal
64021 2 SD 3105-FABRIC WORKING WL-11 Full-time Nonseasonal
64348 3 SC 3101-MISC FABRIC AND LEATHER WORK WG-01 Part-time Nonseasonal
64386 3 SJ 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE DE-01 Part-time Nonseasonal
64539 2 SA 1550-COMPUTER SCIENCE DB-03 Part-time Nonseasonal
64565 2 SC 0830-MECHANICAL ENGINEERING DB-02 Part-time Nonseasonal
64776 2 SJ 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM DJ-04 Part-time Nonseasonal
65181 4 SC 1510-ACTUARIAL SCIENCE GS-15 Intermittent Nonseasonal
65336 1 SC 0110-ECONOMIST AD-00 Intermittent Nonseasonal
65528 1 SA 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... ZA-02 Full-time Nonseasonal
65673 2 SD 1361-NAVIGATIONAL INFORMATION ZA-03 Full-time Nonseasonal
65698 3 SC 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE ZP-01 Full-time Nonseasonal
65736 3 SD 0817-SURVEY TECHNICAL ZT-02 Full-time Nonseasonal
65783 3 SD 5786-SMALL CRAFT OPERATING WG-08 Part-time Nonseasonal
65909 2 SK 1530-STATISTICS ZP-04 Intermittent Nonseasonal
65913 4 SJ 0410-ZOOLOGY ZP-02 Full-time Nonseasonal
65949 1 SD 1382-FOOD TECHNOLOGY ZP-05 Full-time Nonseasonal
66022 2 SC 9932-FIRST ASSISTANT ENGINEER WM-11 Full-time Nonseasonal
66070 2 SA 0505-FINANCIAL MANAGEMENT ZA-05 Full-time Nonseasonal
66108 2 SA 0361-EQUAL OPPORTUNITY ASSISTANCE ZS-04 Full-time Nonseasonal
66142 3 SK 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM ZA-05 Part-time Nonseasonal
66160 1 SC 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... ZP-02 Full-time Seasonal
66266 4 SD 1016-MUSEUM SPECIALIST AND TECHNICIAN ZA-03 Full-time Nonseasonal
66322 3 SJ 1140-TRADE SPECIALIST ED-00 Intermittent Nonseasonal
66328 2 SD 1801-GENERAL INSPECTION, INVESTIGATION, ENFORC... GM-15 Full-time Nonseasonal
66555 2 SC 1224-PATENT EXAMINING GS-09 Full-time Seasonal
66966 3 SC 1222-PATENT ATTORNEY AD-00 Intermittent Nonseasonal
67005 1 SC 1299-COPYRIGHT AND PATENT STUDENT TRAINEE GS-04 Full-time Nonseasonal
67046 2 SC 1320-CHEMISTRY ZP-04 Full-time Seasonal
67055 2 SC 0341-ADMINISTRATIVE OFFICER ZA-02 Part-time Nonseasonal
67061 3 SA 0203-HUMAN RESOURCES ASSISTANCE ZS-04 Part-time Nonseasonal
67064 3 SC 0201-HUMAN RESOURCES MANAGEMENT ZA-03 Intermittent Nonseasonal
67109 4 SJ 2210-INFORMATION TECHNOLOGY MANAGEMENT ZP-05 Intermittent Nonseasonal
67139 4 SC 1310-PHYSICS ZP-03 Part-time Nonseasonal
67145 4 SC 0342-SUPPORT SERVICES ADMINISTRATION ZS-02 Full-time Nonseasonal
67162 4 SK 0809-CONSTRUCTION CONTROL TECHNICAL ZT-02 Full-time Nonseasonal
67169 3 SC 0804-FIRE PROTECTION ENGINEERING ZP-03 Full-time Nonseasonal
67178 4 SC 0342-SUPPORT SERVICES ADMINISTRATION ZS-03 Full-time Nonseasonal
67561 1 SD 1531-STATISTICAL ASSISTANT GG-05 Full-time Seasonal
67840 1 SD 1530-STATISTICS GG-15 Full-time Nonseasonal
69645 3 SA 0201-HUMAN RESOURCES MANAGEMENT GS-07 Full-time Seasonal
69678 3 SC 1371-CARTOGRAPHIC TECHNICIAN GS-04 Full-time Seasonal
70986 2 SA 1529-MATHEMATICAL STATISTICS GS-11 Full-time Seasonal
72229 4 SC 1099-INFORMATION AND ARTS STUDENT TRAINEE CT-04 Full-time Nonseasonal
72291 2 SA 0132-INTELLIGENCE CU-15 Full-time Nonseasonal
72302 2 SD 0905-GENERAL ATTORNEY CU-14 Part-time Nonseasonal
72317 3 SD 0580-CREDIT UNION EXAMINER CU-14 Part-time Nonseasonal
72323 3 SA 0260-EQUAL EMPLOYMENT OPPORTUNITY CU-15 Full-time Nonseasonal
72335 1 SD 0510-ACCOUNTING CU-15 Full-time Nonseasonal
72337 2 SD 1102-CONTRACTING CU-13 Full-time Nonseasonal
72428 2 SC 1102-CONTRACTING NH-04 Part-time Job Sharer Nonseasonal
73089 2 SC 1082-WRITING AND EDITING AD-01 Full-time Nonseasonal
73246 3 SJ 0203-HUMAN RESOURCES ASSISTANCE GS-07 Intermittent Nonseasonal
73318 2 SC 1999-QUALITY INSPECTION STUDENT TRAINEE GS-03 Full-time Nonseasonal
74032 4 SJ 1107-PROPERTY DISPOSAL CLERICAL AND TECHNICIAN GS-04 Full-time Nonseasonal
74457 3 SC 2032-PACKAGING GS-14 Part-time Nonseasonal
75550 1 SD 0080-SECURITY ADMINISTRATION IE-00 Full-time Nonseasonal
75574 4 SD 0306-GOVERNMENT INFORMATION SPECIALIST GG-14 Full-time Nonseasonal
75624 4 SC 0806-MATERIALS ENGINEERING AD-00 Full-time Nonseasonal
75634 1 SJ 0830-MECHANICAL ENGINEERING EE-00 Full-time Nonseasonal
75813 1 SC 0631-OCCUPATIONAL THERAPIST AD-16 Full-time Seasonal
75845 4 SH 1710-EDUCATION AND VOCATIONAL TRAINING AD-13 Part-time Seasonal
76307 4 SC 0665-SPEECH PATHOLOGY AND AUDIOLOGY AD-14 Full-time Seasonal
76420 4 SH 1710-EDUCATION AND VOCATIONAL TRAINING AD-14 Part-time Nonseasonal
76667 3 SC 0640-HEALTH AID AND TECHNICIAN GS-04 Part-time Seasonal
76735 4 SJ 0610-NURSE AD-11 Intermittent Nonseasonal
76771 3 SC 1710-EDUCATION AND VOCATIONAL TRAINING AD-00 Part-time Seasonal
77484 2 SC 0808-ARCHITECTURE NH-02 Full-time Nonseasonal
77695 3 SJ 7408-FOOD SERVICE WORKING WL-02 Part-time Nonseasonal
77887 1 SF 1101-GENERAL BUSINESS AND INDUSTRY GS-02 Full-time Nonseasonal
77916 1 SJ 1101-GENERAL BUSINESS AND INDUSTRY GS-01 Full-time Nonseasonal
80303 4 SC 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE GS-05 Full-time Seasonal
83224 4 SD 1101-GENERAL BUSINESS AND INDUSTRY AD-02 Full-time Nonseasonal
83250 2 SD 0346-LOGISTICS MANAGEMENT AD-03 Full-time Nonseasonal
83404 1 SA 1799-EDUCATION STUDENT TRAINEE NJ-02 Full-time Nonseasonal
83422 2 SC 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM NH-04 Intermittent Nonseasonal
83425 2 SD 0896-INDUSTRIAL ENGINEERING AD-12 Full-time Nonseasonal
83442 1 SJ 1701-GENERAL EDUCATION AND TRAINING AD-22 Intermittent Nonseasonal
84642 4 SC 1060-PHOTOGRAPHY GS-12 Part-time Nonseasonal
85721 3 SD 0610-NURSE GL-10 Part-time Nonseasonal
85965 2 SC 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE GL-04 Part-time Nonseasonal
86905 4 SJ 0299-HUMAN RESOURCES MANAGEMENT STUDENT TRAINEE GL-05 Full-time Nonseasonal
... ... ... ... ... ...
138901 2 SC 1550-COMPUTER SCIENCE EG-00 Intermittent Nonseasonal
138906 3 SJ 0018-SAFETY AND OCCUPATIONAL HEALTH MANAGEMENT GS-15 Intermittent Nonseasonal
138918 3 SJ 1520-MATHEMATICS EE-00 Intermittent Nonseasonal
138947 2 SC 1520-MATHEMATICS EG-00 Intermittent Nonseasonal
138988 1 SD 0170-HISTORY AD-04 Full-time Nonseasonal
139029 2 SC 1040-LANGUAGE SPECIALIST GS-07 Part-time Nonseasonal
139618 1 SJ 1550-COMPUTER SCIENCE EF-00 Intermittent Nonseasonal
140156 4 SJ 0020-COMMUNITY PLANNING EE-00 Full-time Nonseasonal
140167 1 SC 1010-EXHIBITS SPECIALIST GS-11 Part-time Nonseasonal
140258 4 SJ 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE GS-02 Part-time Nonseasonal
140910 1 SK 1301-GENERAL PHYSICAL SCIENCE AJ-00 Full-time Nonseasonal
140921 3 SD 0080-SECURITY ADMINISTRATION EG-00 Intermittent Nonseasonal
140948 4 SC 0999-LEGAL OCCUPATIONS STUDENT TRAINEE GG-07 Full-time Nonseasonal
140949 4 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GG-05 Full-time Nonseasonal
140981 4 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GG-05 Full-time Nonseasonal
140984 4 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GG-09 Full-time Nonseasonal
140987 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-03 Full-time Nonseasonal
140992 4 SC 0999-LEGAL OCCUPATIONS STUDENT TRAINEE GG-09 Full-time Nonseasonal
141000 4 SJ 0343-MANAGEMENT AND PROGRAM ANALYSIS GG-07 Part-time Nonseasonal
141005 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-05 Full-time Nonseasonal
141010 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-07 Full-time Nonseasonal
141027 4 SC 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE GG-07 Full-time Nonseasonal
141029 4 SC 1399-PHYSICAL SCIENCE STUDENT TRAINEE GG-07 Full-time Nonseasonal
141056 2 SD 0482-FISH BIOLOGY GG-15 Full-time Nonseasonal
141057 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-04 Full-time Nonseasonal
141071 4 SJ 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-05 Full-time Nonseasonal
141081 4 SJ 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE GG-05 Part-time Nonseasonal
141100 4 SJ 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE GG-05 Full-time Nonseasonal
141128 3 SJ 0801-GENERAL ENGINEERING GG-15 Intermittent Nonseasonal
141145 2 SJ 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-05 Full-time Nonseasonal
141150 1 SJ 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE GG-09 Full-time Nonseasonal
141153 3 SD 1301-GENERAL PHYSICAL SCIENCE GG-15 Part-time Nonseasonal
141852 3 SD 1811-CRIMINAL INVESTIGATION IE-00 Full-time Nonseasonal
141986 2 SD 0332-COMPUTER OPERATION NC-03 Full-time Nonseasonal
141991 3 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE NR-01 Full-time Nonseasonal
142033 2 SA 2091-SALES STORE CLERICAL NC-01 Full-time Nonseasonal
142047 1 SD 0690-INDUSTRIAL HYGIENE NO-04 Full-time Nonseasonal
142061 3 SC 0999-LEGAL OCCUPATIONS STUDENT TRAINEE NC-01 Full-time Nonseasonal
142069 4 SC 1399-PHYSICAL SCIENCE STUDENT TRAINEE NP-01 Full-time Nonseasonal
142072 4 SJ 0855-ELECTRONICS ENGINEERING NP-03 Intermittent Nonseasonal
142075 3 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE NR-01 Part-time Nonseasonal
142111 2 SA 0809-CONSTRUCTION CONTROL TECHNICAL NR-03 Full-time Nonseasonal
142119 4 SC 1310-PHYSICS NP-04 Part-time Nonseasonal
142154 3 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE NP-02 Part-time Nonseasonal
142163 1 SJ 1411-LIBRARY TECHNICIAN NC-01 Full-time Nonseasonal
142168 2 SC 0086-SECURITY CLERICAL AND ASSISTANCE NC-01 Full-time Nonseasonal
142203 1 SJ 1399-PHYSICAL SCIENCE STUDENT TRAINEE NR-02 Part-time Nonseasonal
143128 2 SC 0638-RECREATION/CREATIVE ARTS THERAPIST GS-11 Part-time Nonseasonal
143264 1 SD 6901-MISC WAREHOUSING AND STOCK HANDLING WG-03 Part-time Nonseasonal
143491 4 SC 0895-INDUSTRIAL ENGINEERING TECHNICAL GS-04 Full-time Nonseasonal
143699 3 SJ 2210-INFORMATION TECHNOLOGY MANAGEMENT DS-01 Full-time Nonseasonal
143939 3 SK 1311-PHYSICAL SCIENCE TECHNICIAN DT-04 Part-time Nonseasonal
144126 4 SC 0801-GENERAL ENGINEERING DP-05 Part-time Nonseasonal
144158 2 SC 0830-MECHANICAL ENGINEERING NM-03 Full-time Nonseasonal
144164 2 SA 1103-INDUSTRIAL PROPERTY MANAGEMENT DA-04 Full-time Nonseasonal
144282 3 SJ 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE DS-01 Full-time Nonseasonal
144314 3 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE DP-03 Full-time Nonseasonal
144465 3 SC 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE DG-01 Full-time Nonseasonal
144579 2 SC 1320-CHEMISTRY DP-02 Part-time Nonseasonal
144679 3 SD 0303-MISCELLANEOUS CLERK AND ASSISTANT DG-06 Full-time Nonseasonal
144866 1 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE DT-01 Part-time Nonseasonal
144902 4 SJ 1710-EDUCATION AND VOCATIONAL TRAINING AD-01 Part-time Nonseasonal
145001 4 SJ 1710-EDUCATION AND VOCATIONAL TRAINING AD-03 Part-time Nonseasonal
145028 3 SC 1710-EDUCATION AND VOCATIONAL TRAINING AD-01 Full-time Seasonal
145194 1 SC 0006-CORRECTIONAL INSTITUTION ADMINISTRATION GS-13 Part-time Nonseasonal
146139 1 SD 6641-ORDNANCE EQUIPMENT MECHANIC WG-12 Full-time Nonseasonal
146276 2 SD 1515-OPERATIONS RESEARCH ND-05 Part-time Nonseasonal
146408 4 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GS-04 Full-time Seasonal
146441 4 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE NT-01 Full-time Nonseasonal
146517 4 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GS-04 Full-time Seasonal
146526 3 SD 4301-MISCELLANEOUS PLIABLE MATERIALS WORK WG-11 Full-time Nonseasonal
146559 2 SC 0415-TOXICOLOGY ND-04 Part-time Nonseasonal
146714 4 SK 1102-CONTRACTING NT-05 Part-time Nonseasonal
146905 4 SC 1222-PATENT ATTORNEY NT-05 Full-time Nonseasonal
146968 2 SC 1515-OPERATIONS RESEARCH ND-05 Part-time Nonseasonal
147275 3 SD 1521-MATHEMATICS TECHNICIAN GS-12 Full-time Nonseasonal
147378 4 SF 3414-MACHINING WG-04 Full-time Nonseasonal
147609 2 SD 5876-ELECTROMOTIVE EQUIPMENT MECHANIC WG-11 Full-time Nonseasonal
148473 3 SC 0804-FIRE PROTECTION ENGINEERING GS-13 Part-time Nonseasonal
148595 3 SD 5407-ELECTRICAL POWER CONTROLLING WS-11 Full-time Nonseasonal
148604 4 SA 0021-COMMUNITY PLANNING TECHNICIAN GS-04 Full-time Nonseasonal
149482 2 SI 5409-WATER TREATMENT PLANT OPERATING WS-11 Full-time Nonseasonal
149539 4 SA 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GG-07 Full-time Nonseasonal
149587 4 SC 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE GS-03 Full-time Seasonal
149648 4 SD 1082-WRITING AND EDITING GS-08 Full-time Nonseasonal
149876 3 SD 5413-FUEL DISTRIBUTION SYSTEM OPERATING WL-09 Full-time Nonseasonal
150646 3 SJ 0801-GENERAL ENGINEERING EE-00 Full-time Nonseasonal
150728 1 SJ 9936-ENGINE MIDSHIPMAN WM-21 Full-time Nonseasonal
150901 1 SJ 9917-DECK MIDSHIPMAN WM-21 Full-time Nonseasonal
151029 2 SJ 9917-DECK MIDSHIPMAN WM-21 Full-time Nonseasonal
151039 2 SJ 9936-ENGINE MIDSHIPMAN WM-21 Full-time Nonseasonal
151282 1 SD 2131-FREIGHT RATE NG-03 Full-time Nonseasonal
151366 1 SA 0260-EQUAL EMPLOYMENT OPPORTUNITY DP-04 Full-time Nonseasonal
151369 1 SI 0361-EQUAL OPPORTUNITY ASSISTANCE NG-02 Full-time Nonseasonal
151387 3 SC 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE NO-03 Full-time Nonseasonal
151397 4 SC 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE NG-01 Part-time Nonseasonal
151625 3 SD 0510-ACCOUNTING NO-06 Full-time Nonseasonal
153743 2 SD 4373-MOLDING WD-06 Full-time Nonseasonal
154266 1 SD 3802-METAL FORGING WL-10 Full-time Nonseasonal
154284 3 SC 3359-INSTRUMENT MECHANIC WG-01 Full-time Seasonal
154346 3 SC 0871-NAVAL ARCHITECTURE GS-12 Part-time Nonseasonal
154627 3 SC 3801-MISCELLANEOUS METAL WORK WG-03 Full-time Seasonal
154637 3 SA 3806-SHEET METAL MECHANIC WG-05 Full-time Seasonal
155027 2 SD 3401-MISCELLANEOUS MACHINE TOOL WORK WS-15 Full-time Nonseasonal
155347 1 SC 3414-MACHINING WG-08 Full-time Seasonal
156304 3 SC 0086-SECURITY CLERICAL AND ASSISTANCE FP-06 Full-time Nonseasonal
156307 4 SD 2130-TRAFFIC MANAGEMENT FP-02 Full-time Nonseasonal
156310 4 SC 1087-EDITORIAL ASSISTANCE FP-07 Full-time Nonseasonal
156321 2 SA 0905-GENERAL ATTORNEY FP-04 Full-time Nonseasonal
156355 3 SA 0510-ACCOUNTING FP-03 Full-time Nonseasonal
156360 2 SI 0303-MISCELLANEOUS CLERK AND ASSISTANT FP-07 Part-time Nonseasonal
156361 3 SC 0303-MISCELLANEOUS CLERK AND ASSISTANT FP-09 Part-time Nonseasonal
156368 4 SC 0669-MEDICAL RECORDS ADMINISTRATION FP-05 Full-time Nonseasonal
156374 2 SJ 0303-MISCELLANEOUS CLERK AND ASSISTANT FP-08 Full-time Nonseasonal
156382 4 SC 1702-EDUCATION AND TRAINING TECHNICIAN FP-06 Full-time Nonseasonal
156448 2 SC 0303-MISCELLANEOUS CLERK AND ASSISTANT FP-08 Full-time Nonseasonal
156454 1 SC 1750-INSTRUCTIONAL SYSTEMS FP-04 Full-time Nonseasonal
156479 4 SA 0260-EQUAL EMPLOYMENT OPPORTUNITY FP-04 Full-time Nonseasonal
156484 1 SA 0201-HUMAN RESOURCES MANAGEMENT FP-02 Part-time Nonseasonal
157610 4 SC 2210-INFORMATION TECHNOLOGY MANAGEMENT SK-16 Part-time Nonseasonal
157665 2 SD 1410-LIBRARIAN SK-15 Full-time Nonseasonal
157751 4 SC 0201-HUMAN RESOURCES MANAGEMENT SK-13 Part-time Nonseasonal
157761 4 SC 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE SK-07 Full-time Nonseasonal
157764 4 SD 0340-PROGRAM MANAGEMENT SK-16 Full-time Nonseasonal
157782 1 SJ 0950-PARALEGAL SPECIALIST SK-07 Full-time Nonseasonal
157790 2 SC 1750-INSTRUCTIONAL SYSTEMS SK-16 Full-time Nonseasonal
157794 2 SC 1410-LIBRARIAN SK-09 Full-time Nonseasonal
157795 3 SI 0080-SECURITY ADMINISTRATION SK-17 Full-time Nonseasonal
157798 2 SK 0201-HUMAN RESOURCES MANAGEMENT SK-14 Part-time Nonseasonal
157823 3 SC 2210-INFORMATION TECHNOLOGY MANAGEMENT SO-01 Full-time Nonseasonal
157834 4 SJ 0501-FINANCIAL ADMINISTRATION AND PROGRAM SK-13 Part-time Nonseasonal
158130 3 SC 0804-FIRE PROTECTION ENGINEERING GS-07 Full-time Nonseasonal
158187 2 SC 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER WL-02 Full-time Nonseasonal
158375 1 SF 0356-DATA TRANSCRIBER GS-07 Full-time Nonseasonal
158552 3 SJ 0130-FOREIGN AFFAIRS GS-15 Intermittent Nonseasonal
158577 3 SC 0130-FOREIGN AFFAIRS AD-00 Full-time Nonseasonal
158579 3 SJ 2032-PACKAGING GS-12 Intermittent Nonseasonal
158583 3 SA 0130-FOREIGN AFFAIRS GG-14 Full-time Nonseasonal
158587 3 SC 0130-FOREIGN AFFAIRS EF-15 Intermittent Nonseasonal
158599 3 SJ 0130-FOREIGN AFFAIRS EF-14 Intermittent Nonseasonal
158622 3 SJ 0080-SECURITY ADMINISTRATION GS-14 Intermittent Nonseasonal
158654 3 SJ 0130-FOREIGN AFFAIRS GS-14 Intermittent Nonseasonal
158692 2 SJ 0318-SECRETARY GS-10 Intermittent Nonseasonal
158775 2 SD 1008-INTERIOR DESIGN GS-15 Full-time Nonseasonal
158847 3 SJ 0391-TELECOMMUNICATIONS GS-11 Intermittent Nonseasonal
158884 3 SD 0150-GEOGRAPHY ES-** Full-time Nonseasonal
158987 2 SD 0132-INTELLIGENCE GM-13 Full-time Nonseasonal
159027 3 SC 0130-FOREIGN AFFAIRS GS-14 Intermittent Nonseasonal
159029 3 SC 1109-GRANTS MANAGEMENT AD-05 Full-time Nonseasonal
159042 3 SJ 1035-PUBLIC AFFAIRS AD-05 Full-time Nonseasonal
159044 3 SC 0306-GOVERNMENT INFORMATION SPECIALIST GS-09 Part-time Nonseasonal
159045 3 SJ 0130-FOREIGN AFFAIRS EF-15 Intermittent Nonseasonal
159103 3 SC 0130-FOREIGN AFFAIRS ED-15 Intermittent Nonseasonal
159147 3 SK 0130-FOREIGN AFFAIRS GS-14 Intermittent Nonseasonal
159217 1 SC 0130-FOREIGN AFFAIRS EF-15 Full-time Nonseasonal
159782 3 SC 0905-GENERAL ATTORNEY AA-06 Intermittent Nonseasonal
159803 2 SJ 0901-GENERAL LEGAL AND KINDRED ADMINISTRATION GS-09 Intermittent Nonseasonal
160700 3 SC 0260-EQUAL EMPLOYMENT OPPORTUNITY GS-15 Intermittent Nonseasonal
160723 3 SJ 0905-GENERAL ATTORNEY GS-12 Intermittent Nonseasonal
161612 2 SC 0998-CLAIMS ASSISTANCE AND EXAMINING GS-07 Intermittent Nonseasonal
164022 2 SI 0105-SOCIAL INSURANCE ADMINISTRATION GS-06 Full-time Nonseasonal
164528 1 SD 0160-CIVIL RIGHTS ANALYSIS ES-** Full-time Nonseasonal
164537 3 SC 0020-COMMUNITY PLANNING GS-12 Intermittent Nonseasonal
164593 3 SC 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE GS-07 Part-time Nonseasonal
164751 3 SC 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM FJ-00 Full-time Nonseasonal
165005 3 SD 0413-PHYSIOLOGY FV-J Full-time Nonseasonal
165047 4 SD 1825-AVIATION SAFETY EV-02 Full-time Nonseasonal
165056 1 SD 0346-LOGISTICS MANAGEMENT FV-G Part-time Nonseasonal
165207 4 SI 0861-AEROSPACE ENGINEERING FG-13 Full-time Nonseasonal
165242 3 SD 1071-AUDIOVISUAL PRODUCTION FV-G Full-time Nonseasonal
165440 4 SD 0601-GENERAL HEALTH SCIENCE FV-G Full-time Nonseasonal
165681 4 SC 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... FV-G Full-time Nonseasonal
165694 4 SK 0343-MANAGEMENT AND PROGRAM ANALYSIS FG-15 Full-time Nonseasonal
165819 3 SC 0343-MANAGEMENT AND PROGRAM ANALYSIS FG-07 Full-time Nonseasonal
166216 2 SJ 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE FV-C Part-time Nonseasonal
166649 2 SD 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... FV-I Full-time Nonseasonal
167164 3 SD 2010-INVENTORY MANAGEMENT FG-09 Full-time Nonseasonal
167232 1 SC 0675-MEDICAL RECORDS TECHNICIAN FV-G Full-time Nonseasonal
167765 4 SA 0810-CIVIL ENGINEERING GS-07 Full-time Seasonal
167983 4 SC 0090-GUIDE GS-01 Full-time Nonseasonal
167984 4 SJ 0090-GUIDE GS-01 Full-time Nonseasonal
167990 3 SD 5786-SMALL CRAFT OPERATING WL-12 Full-time Seasonal
168609 1 SA 1520-MATHEMATICS OR-51 Full-time Nonseasonal
169045 4 SC 0356-DATA TRANSCRIBER GS-03 Full-time Seasonal
169402 4 SJ 0356-DATA TRANSCRIBER GS-03 Part-time Nonseasonal
169897 3 SD 0341-ADMINISTRATIVE OFFICER IR-SM Full-time Nonseasonal
170587 3 SJ 0303-MISCELLANEOUS CLERK AND ASSISTANT GS-02 Full-time Seasonal
170682 4 SA 0501-FINANCIAL ADMINISTRATION AND PROGRAM GS-05 Full-time Seasonal
171044 3 SC 0303-MISCELLANEOUS CLERK AND ASSISTANT GS-02 Full-time Seasonal
172568 3 SJ 2005-SUPPLY CLERICAL AND TECHNICIAN GS-04 Intermittent Nonseasonal
172848 1 SI 0356-DATA TRANSCRIBER GS-03 Full-time Seasonal
173512 2 SD 1101-GENERAL BUSINESS AND INDUSTRY GS-09 Part-time Job Sharer Nonseasonal
174326 4 SC 0592-TAX EXAMINING GS-05 Part-time Seasonal
175009 2 SD 1397-DOCUMENT ANALYSIS IR-FM Full-time Nonseasonal
175175 2 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... GS-06 Full-time Seasonal
176572 3 SC 0501-FINANCIAL ADMINISTRATION AND PROGRAM GS-09 Part-time Seasonal
176724 4 SC 0356-DATA TRANSCRIBER GS-02 Full-time Seasonal
177579 1 SJ 0512-INTERNAL REVENUE AGENT GS-13 Intermittent Nonseasonal
178189 1 SC 0356-DATA TRANSCRIBER GS-02 Full-time Seasonal
178500 4 SI 3869-METAL FORMING MACHINE OPERATING WG-06 Intermittent Nonseasonal
178757 1 SD 0511-AUDITING NB-07 Full-time Nonseasonal
178763 4 SC 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... NB-03 Full-time Nonseasonal
178811 3 SC 1160-FINANCIAL ANALYSIS NB-06 Part-time Nonseasonal
178820 4 SC 0110-ECONOMIST NB-06 Part-time Nonseasonal
178862 1 SD 0986-LEGAL ASSISTANCE NB-04 Full-time Nonseasonal
178979 1 SA 0303-MISCELLANEOUS CLERK AND ASSISTANT NB-02 Part-time Nonseasonal
179028 1 SC 0570-FINANCIAL INSTITUTION EXAMINING NB-06 Intermittent Nonseasonal
179323 4 SC 0511-AUDITING ES-** Intermittent Nonseasonal
180727 4 SC 0996-VETERANS CLAIMS EXAMINING GS-10 Part-time Nonseasonal
182096 1 SJ 0503-FINANCIAL CLERICAL AND ASSISTANCE GS-06 Intermittent Nonseasonal
182785 1 SJ 0621-NURSING ASSISTANT AD-00 Full-time Nonseasonal
183419 1 SJ 4102-PAINTING WB-00 Intermittent Nonseasonal
184918 1 SC 0661-PHARMACY TECHNICIAN GS-02 Intermittent Nonseasonal
185606 1 SC 0187-SOCIAL SERVICES GS-07 Intermittent Nonseasonal
185612 1 SC 7305-LAUNDRY MACHINE OPERATING WL-04 Full-time Nonseasonal
185619 4 SI 0102-SOCIAL SCIENCE AID AND TECHNICIAN GS-02 Part-time Nonseasonal
186470 3 SI 0661-PHARMACY TECHNICIAN GS-02 Part-time Nonseasonal
187280 2 SC 0625-AUTOPSY ASSISTANT GS-04 Intermittent Nonseasonal
188209 3 SC 7305-LAUNDRY MACHINE OPERATING WG-04 Part-time Nonseasonal
188290 3 SC 0083-POLICE GS-07 Intermittent Nonseasonal
189645 1 SD 1601-EQUIPMENT FACILITIES, AND SERVICES GS-11 Intermittent Nonseasonal
190784 3 SC 0180-PSYCHOLOGY AD-00 Intermittent Nonseasonal
191199 3 SJ 0199-SOCIAL SCIENCE STUDENT TRAINEE AD-00 Part-time Nonseasonal
193872 4 SJ 0185-SOCIAL WORK GS-02 Intermittent Nonseasonal
194936 2 SJ 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE GS-02 Intermittent Nonseasonal
195894 1 SJ 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... GS-10 Intermittent Nonseasonal
195895 1 SC 0299-HUMAN RESOURCES MANAGEMENT STUDENT TRAINEE GS-09 Part-time Nonseasonal
196227 4 SD 7301-MISC LAUNDRY, DRY CLEANING, AND PRESSING WS-08 Full-time Nonseasonal
196320 2 SC 0681-DENTAL ASSISTANT GS-08 Part-time Nonseasonal
196786 1 SC 0683-DENTAL LABORATORY AID AND TECHNICIAN GS-05 Part-time Nonseasonal
198299 4 SC 0699-MEDICAL AND HEALTH STUDENT TRAINEE GS-02 Full-time Nonseasonal
198505 4 SC 0530-CASH PROCESSING GS-05 Part-time Nonseasonal
199799 4 SC 0181-PSYCHOLOGY AID AND TECHNICIAN GS-04 Intermittent Nonseasonal
200273 2 SJ 0335-COMPUTER CLERK AND ASSISTANT GS-03 Intermittent Nonseasonal
200845 4 SJ 0525-ACCOUNTING TECHNICIAN GS-04 Intermittent Nonseasonal
201133 1 SC 7404-COOKING WG-07 Full-time Nonseasonal
201186 1 SD 0639-EDUCATIONAL THERAPIST GS-09 Full-time Nonseasonal
202022 4 SJ 0601-GENERAL HEALTH SCIENCE GS-05 Intermittent Nonseasonal
202948 2 SD 0637-MANUAL ARTS THERAPIST GS-11 Full-time Nonseasonal
203694 2 SC 0530-CASH PROCESSING VC-02 Intermittent Nonseasonal
203728 2 SC 5703-MOTOR VEHICLE OPERATING WG-02 Part-time Nonseasonal
203929 3 SC 0669-MEDICAL RECORDS ADMINISTRATION GS-11 Intermittent Nonseasonal
204060 2 SD 6901-MISC WAREHOUSING AND STOCK HANDLING WD-07 Full-time Nonseasonal
204563 2 SJ 0644-MEDICAL TECHNOLOGIST GS-07 Intermittent Nonseasonal
206370 1 SJ 0670-HEALTH SYSTEM ADMINISTRATION AD-00 Intermittent Nonseasonal
207091 1 SJ 0605-NURSE ANESTHETIST (TITLE 38) AD-00 Part-time Nonseasonal
210811 3 SD 4010-PRESCRIPTION EYEGLASS MAKING WL-09 Full-time Nonseasonal
211162 3 SC 0699-MEDICAL AND HEALTH STUDENT TRAINEE GS-06 Full-time Nonseasonal
211308 1 SC 0645-MEDICAL TECHNICIAN GS-01 Intermittent Nonseasonal
214123 1 SI 5406-UTILITY SYSTEMS OPERATING WS-12 Full-time Nonseasonal

853 rows × 5 columns

These 1293 separation observations do not have coverage within the EMP Dataset, thus, we will remove these observations as out of scope demographic in our analysis. Any attempt in predicting these values will not have enough data to support a significant response.

In [14]:
OPMDataMerged = OPMDataMerged[OPMDataMerged["IndAvgSalary"].notnull()]

print(len(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()]))

print(len(OPMDataMerged))
0
8170907


Placeholder Chunks for Data Quality check of salary against GS Grade Level Ranges



In [15]:
# Placeholder Chunks for Data Quality check of salary against GS Grade Level Ranges
In [ ]:
 

We are iterested to see how federal pension plans may impact attrition in this dataset. An interesting attribute to complement Length of service, is Years to Retirement. Utilizing a FERS retirement eligibility baseline of 57 years of age for all observations, and the lower limitation of age level ranges we compute a numeric value for length of retirement.

In [16]:
#Add Column YearsToRetirement

"""
    AGELVL,AGELVLT
    A,Less than 20
    B,20-24
    C,25-29
    D,30-34
    E,35-39
    F,40-44
    G,45-49
    H,50-54
    I,55-59
    J,60-64
    K,65 or more
    Z,Unspecified
"""
OPMDataMerged["LowerLimitAge"] = np.where(OPMDataMerged["AGELVL"]=="B", 20,
                                                np.where(OPMDataMerged["AGELVL"]=="C", 25,
                                                         np.where(OPMDataMerged["AGELVL"]=="D", 30,
                                                                  np.where(OPMDataMerged["AGELVL"]=="E", 35,
                                                                           np.where(OPMDataMerged["AGELVL"]=="F", 40,
                                                                                    np.where(OPMDataMerged["AGELVL"]=="G", 45,
                                                                                             np.where(OPMDataMerged["AGELVL"]=="H", 50,
                                                                                                      np.where(OPMDataMerged["AGELVL"]=="I", 55,
                                                                                                               np.where(OPMDataMerged["AGELVL"]=="J", 60,
                                                                                                                        np.where(OPMDataMerged["AGELVL"]=="K", 65,
                                                                                                                                 np.nan
                                                                                                                                )
                                                                                                                        )
                                                                                                               )
                                                                                                      )
                                                                                            )
                                                                                   )
                                                                          )
                                                                 )
                                                        )
                                               )  

retAge = 57

OPMDataMerged["YearsToRetirement"] = np.where(OPMDataMerged["AGELVL"]=="B", retAge-20,
                                                np.where(OPMDataMerged["AGELVL"]=="C", retAge-25,
                                                         np.where(OPMDataMerged["AGELVL"]=="D", retAge-30,
                                                                  np.where(OPMDataMerged["AGELVL"]=="E", retAge-35,
                                                                           np.where(OPMDataMerged["AGELVL"]=="F", retAge-40,
                                                                                    np.where(OPMDataMerged["AGELVL"]=="G", retAge-45,
                                                                                             np.where(OPMDataMerged["AGELVL"]=="H", retAge-50,
                                                                                                      np.where(OPMDataMerged["AGELVL"]=="I", retAge-55,
                                                                                                               np.where(OPMDataMerged["AGELVL"]=="J", retAge-60,
                                                                                                                        np.where(OPMDataMerged["AGELVL"]=="K", retAge-65,
                                                                                                                                 np.nan
                                                                                                                                )
                                                                                                                        )
                                                                                                               )
                                                                                                      )
                                                                                            )
                                                                                   )
                                                                          )
                                                                 )
                                                        )
                                               )  

print("Null Values for LowerLimitAge: " + str(len(OPMDataMerged[OPMDataMerged["LowerLimitAge"].isnull()])))
print("Null Values for YearsToRetirement: " + str(len(OPMDataMerged[OPMDataMerged["YearsToRetirement"].isnull()])))

display(OPMDataMerged.head())
display(OPMDataMerged.tail())
Null Values for LowerLimitAge: 0
Null Values for YearsToRetirement: 0
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement
0 AA00 SC 201507 C M 11 A 11 0905 1 GS-11 F 40 F 1.0 63722.0 0.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 4 25-29 Less than 1 year 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 2 Non-permanent 40-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 205.0 1319 64540.593830 -818.593830 25.0 32.0
1 AA00 SC 201506 D F 15 C 11 0905 1 GS-15 L 30 F 1.0 126245.0 4.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 3 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $120,000 - $129,999 1 Permanent 30-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 207.0 1132 149864.298504 -23619.298504 30.0 27.0
2 AF** SA 201503 H M 11 C 48 2210 2 GS-11 F 10 F 1.0 66585.0 4.9 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF**-INVALID 2 50-54 3 - 4 years 1 United States 48-TEXAS 1 White Collar 22 22xx-INFORMATION TECHNOLOGY 2210-INFORMATION TECHNOLOGY MANAGEMENT Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 439.0 1087 71530.963755 -4945.963755 50.0 7.0
3 AF02 SD 201506 I M 15 J 35 0301 2 GS-15 O 10 F 1.0 156737.0 39.8 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF02-AIR FORCE INSPECTION AGENCY (FO) 3 55-59 35 years or more 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $150,000 - $159,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 670.0 265 146735.220304 10001.779696 55.0 2.0
4 AF03 SC 201509 H M 13 B 06 0301 2 GS-13 I 15 F 1.0 92973.0 1.0 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... 4 50-54 1 - 2 years 1 United States 06-CALIFORNIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $90,000 - $99,999 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 721.0 1853 101641.124025 -8668.124025 50.0 7.0
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement
8223188 ZU00 NS 201509 D NaN NaN C 11 0301 2 AD-00 G 48 F NaN 76377.0 4.8 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $70,000 - $79,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -39463.182250 30.0 27.0
8223189 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 M 48 F NaN 139517.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $130,000 - $139,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 23676.817750 65.0 -8.0
8223190 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 O 48 F NaN 158671.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $150,000 - $159,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 42830.817750 65.0 -8.0
8223191 ZU00 NS 201509 B NaN NaN B 11 0301 2 AD-00 C 48 F NaN 36244.0 1.6 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 20-24 1 - 2 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $30,000 - $39,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -79596.182250 20.0 37.0
8223192 ZU00 NS 201509 E NaN NaN D 11 0505 2 AD-00 I 48 F NaN 99288.0 5.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 35-39 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 05 05xx-ACCOUNTING AND BUDGET 0505-FINANCIAL MANAGEMENT Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $90,000 - $99,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 7.0 1391 148382.833333 -49094.833333 35.0 22.0

Pull Bureau of Labor Statistics data

In addition to the OPM data, we merge 10 attributes from the Bureau of Labor Statistics (BLS). Data is sourced from Federal Government industry codes across all regions. Although assumed to be highly correlated, we source both Level (Total number) and Rate (Percentage of Level to total employment and / or job openings) for the following statistics: 1) Job Openings, 2) Layoffs, 3) Quits, 4) Total Separations, and 5) Other Separations. While Rate paints an aggregated, holistic picture for job market trends, Level provides a raw count for total separations alone. Both these statistics were captured by a monthly aggregate and merged to the OPM data by their respective months.

In [17]:
%%time

def bls(series, start, end):
    headers = {'Content-type': 'application/json'}
    sID   = []
    
    for i in range(0,len(series)):
        sID.append(series[i][0])
    
    data = json.dumps({"seriesid": sID,
                       "startyear":start,
                       "endyear":end,
                       "catalog":False,
                       "calculations":False,
                       "annualaverage":False,
                       "registrationkey":"7a89c8d7979349fba8914b8be16a1646"})
    
    p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)
    json_data = json.loads(p.text)
    bls = []
    for series in json_data['Results']['series']:
        #x=prettytable.PrettyTable(["series id","year","period","value","footnotes"])
        result = pd.DataFrame(columns=["series id","year","period","value","footnotes"])
        seriesId = series['seriesID']
        for item in series['data']:
            year = item['year']
            period = item['period']
            value = item['value']
            footnotes=""
            for footnote in item['footnotes']:
                if footnote:
                    footnotes = footnotes + footnote['text'] + ','
            if 'M01' <= period <= 'M12':
                #x.add_row([seriesId,year,period,value,footnotes[0:-1]])
                y = pd.DataFrame({"series id" : seriesId,
                                  "year" : year,
                                  "period" : period,
                                  "value" : value,
                                  "footnotes" : footnotes}, index = [0])
                result = result.append(y, ignore_index = True)
        bls.append(result)
    return(bls)
CPU times: user 3 µs, sys: 0 ns, total: 3 µs
Wall time: 5.72 µs
In [18]:
%%time

seriesList = [
              ['JTU91000000JOL','BLS_FEDERAL_JobOpenings_Level'],
              ['JTU91000000LDL','BLS_FEDERAL_Layoffs_Level'],
              ['JTU91000000OSL','BLS_FEDERAL_OtherSep_Level'],
              ['JTU91000000QUL','BLS_FEDERAL_Quits_Level'],
              ['JTU91000000TSL','BLS_FEDERAL_TotalSep_Level'],
              ['JTU91000000JOR','BLS_FEDERAL_JobOpenings_Rate'],
              ['JTU91000000LDR','BLS_FEDERAL_Layoffs_Rate'],
              ['JTU91000000OSR','BLS_FEDERAL_OtherSep_Rate'],
              ['JTU91000000QUR','BLS_FEDERAL_Quits_Rate'],
              ['JTU91000000TSR','BLS_FEDERAL_TotalSep_Rate']
             ]

# Pull job openings and labor turnover data
JTL = bls(seriesList, "2014", "2015")

seriesList = pd.DataFrame(seriesList, columns = ["series id","sName"])

##We need to replace these with actual Descriptor Column Names

for i in range(0,len(seriesList)):
    
    JTL[i] = JTL[i].merge(seriesList, on = "series id", how = 'inner')

    if len(JTL[i]) >0:
        name = JTL[i]["sName"].drop_duplicates().values[0]
    else:
        name = str(i)

    JTL[i][name] = JTL[i]["value"].apply(pd.to_numeric)
    JTL[i]["DATECODE"] = JTL[i]["year"] + JTL[i]["period"].str[-2:]
    del JTL[i]["value"]
    del JTL[i]["year"]
    del JTL[i]["period"]
    del JTL[i]["series id"]
    del JTL[i]["footnotes"]
    del JTL[i]["sName"]
    
    
    OPMDataMerged = OPMDataMerged.merge(JTL[i], on = "DATECODE", how = 'left')
    display(JTL[i].head())
    
BLS_FEDERAL_OtherSep_Rate DATECODE
0 0.4 201512
1 0.4 201511
2 0.4 201510
3 0.4 201509
4 0.5 201508
BLS_FEDERAL_Quits_Rate DATECODE
0 0.4 201512
1 0.4 201511
2 0.6 201510
3 0.5 201509
4 0.6 201508
BLS_FEDERAL_TotalSep_Level DATECODE
0 37 201512
1 35 201511
2 45 201510
3 38 201509
4 41 201508
BLS_FEDERAL_JobOpenings_Rate DATECODE
0 2.9 201512
1 2.6 201511
2 2.4 201510
3 1.9 201509
4 2.3 201508
BLS_FEDERAL_OtherSep_Level DATECODE
0 12 201512
1 10 201511
2 12 201510
3 12 201509
4 14 201508
BLS_FEDERAL_Quits_Level DATECODE
0 11 201512
1 10 201511
2 16 201510
3 14 201509
4 17 201508
BLS_FEDERAL_JobOpenings_Level DATECODE
0 83 201512
1 73 201511
2 68 201510
3 55 201509
4 67 201508
BLS_FEDERAL_Layoffs_Rate DATECODE
0 0.5 201512
1 0.6 201511
2 0.6 201510
3 0.4 201509
4 0.3 201508
BLS_FEDERAL_Layoffs_Level DATECODE
0 15 201512
1 15 201511
2 18 201510
3 12 201509
4 10 201508
BLS_FEDERAL_TotalSep_Rate DATECODE
0 1.3 201512
1 1.3 201511
2 1.6 201510
3 1.4 201509
4 1.5 201508
CPU times: user 36.9 s, sys: 10.4 s, total: 47.2 s
Wall time: 47.8 s
In [19]:
display(OPMDataMerged.head())
display(OPMDataMerged.tail())
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate
0 AA00 SC 201507 C M 11 A 11 0905 1 GS-11 F 40 F 1.0 63722.0 0.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 4 25-29 Less than 1 year 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 2 Non-permanent 40-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 205.0 1319 64540.593830 -818.593830 25.0 32.0 0.4 0.5 34 2.6 11 13 74 0.4 10 1.2
1 AA00 SC 201506 D F 15 C 11 0905 1 GS-15 L 30 F 1.0 126245.0 4.8 4 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... 3 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $120,000 - $129,999 1 Permanent 30-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 207.0 1132 149864.298504 -23619.298504 30.0 27.0 0.4 0.5 34 2.3 12 13 65 0.4 10 1.2
2 AF** SA 201503 H M 11 C 48 2210 2 GS-11 F 10 F 1.0 66585.0 4.9 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF**-INVALID 2 50-54 3 - 4 years 1 United States 48-TEXAS 1 White Collar 22 22xx-INFORMATION TECHNOLOGY 2210-INFORMATION TECHNOLOGY MANAGEMENT Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $60,000 - $69,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 439.0 1087 71530.963755 -4945.963755 50.0 7.0 0.3 0.4 31 3.0 9 10 86 0.5 12 1.1
3 AF02 SD 201506 I M 15 J 35 0301 2 GS-15 O 10 F 1.0 156737.0 39.8 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF02-AIR FORCE INSPECTION AGENCY (FO) 3 55-59 35 years or more 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $150,000 - $159,999 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 670.0 265 146735.220304 10001.779696 55.0 2.0 0.4 0.5 34 2.3 12 13 65 0.4 10 1.2
4 AF03 SC 201509 H M 13 B 06 0301 2 GS-13 I 15 F 1.0 92973.0 1.0 1 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... 4 50-54 1 - 2 years 1 United States 06-CALIFORNIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans GS GS-GENERAL SCHEDULE $90,000 - $99,999 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 721.0 1853 101641.124025 -8668.124025 50.0 7.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
AGYSUB SEP DATECODE AGELVL GENDER GSEGRD LOSLVL LOC OCC PATCO PPGRD SALLVL TOA WORKSCH COUNT SALARY LOS AGYTYP AGYTYPT AGY AGYT AGYSUBT QTR AGELVLT LOSLVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT OCCT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT PAYPLAN PAYPLANT SALLVLT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate
8170902 ZU00 NS 201509 D NaN NaN C 11 0301 2 AD-00 G 48 F NaN 76377.0 4.8 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 30-34 3 - 4 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $70,000 - $79,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -39463.182250 30.0 27.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
8170903 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 M 48 F NaN 139517.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $130,000 - $139,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 23676.817750 65.0 -8.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
8170904 ZU00 NS 201509 K NaN NaN D 11 0301 2 AD-00 O 48 F NaN 158671.0 7.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 65 or more 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $150,000 - $159,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 42830.817750 65.0 -8.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
8170905 ZU00 NS 201509 B NaN NaN B 11 0301 2 AD-00 C 48 F NaN 36244.0 1.6 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 20-24 1 - 2 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $30,000 - $39,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 721.0 1391 115840.182250 -79596.182250 20.0 37.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
8170906 ZU00 NS 201509 E NaN NaN D 11 0505 2 AD-00 I 48 F NaN 99288.0 5.0 4 Small Independent Agencies (less than 100 empl... ZU ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION 4 35-39 5 - 9 years 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 05 05xx-ACCOUNTING AND BUDGET 0505-FINANCIAL MANAGEMENT Administrative 3 Other White Collar Pay Plans 31 Governmentwide or Multi-Agency Plans AD AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... $90,000 - $99,999 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 7.0 1391 148382.833333 -49094.833333 35.0 22.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4
In [ ]:
 
In [20]:
display(pd.DataFrame({'StratCount' : OPMDataMerged.groupby(["SEP"]).size()}).reset_index())
SEP StratCount
0 NS 7957918
1 SA 26945
2 SB 333
3 SC 66248
4 SD 56820
5 SE 1260
6 SF 4100
7 SG 1467
8 SH 400
9 SI 9728
10 SJ 42754
11 SK 2892
12 SL 42

Preliminary EDA

In terms of data exploration, we first investigate numeric type attributes. Relationships, distributions, and correlation values are reviewed.

A new binary separation attribute is created to indicate whether non-sep or sep for EDA correlation purposes

In [21]:
#%%time
#
#
#cols = list(SampledOPMData.select_dtypes(include=['float64', 'int64']))
#cols.remove('COUNT')
#cols.remove('BLS_FEDERAL_OtherSep_Rate')
#cols.remove('BLS_FEDERAL_Quits_Rate')
#cols.remove('BLS_FEDERAL_TotalSep_Level')
#cols.remove('BLS_FEDERAL_JobOpenings_Rate')
#cols.remove('BLS_FEDERAL_OtherSep_Level')
#cols.remove('BLS_FEDERAL_Quits_Level')
#cols.remove('BLS_FEDERAL_JobOpenings_Level')
#cols.remove('BLS_FEDERAL_Layoffs_Rate')
#cols.remove('BLS_FEDERAL_Layoffs_Level')
#cols.remove('BLS_FEDERAL_TotalSep_Rate')
#cols.append('SEP')
#display(cols)
#
#plotNumeric = SampledOPMData[cols]
#
## Create binary separation attribute for EDA correlation review
##plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
##plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
##plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
#AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
#display(AttSplit.head())
#plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe
#
#display(plotNumeric.head())
#print("plotNumeric has {0} Records".format(len(plotNumeric)))
##print(plotNumeric.SEP_bin.dtype)
In [22]:
%%time


cols = list(OPMDataMerged.select_dtypes(include=['float64', 'int64']))
cols.remove('COUNT')
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)

plotNumeric = OPMDataMerged[cols]

# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe

display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['SALARY',
 'LOS',
 'SEPCount_EFDATE_OCC',
 'SEPCount_EFDATE_LOC',
 'IndAvgSalary',
 'SalaryOverUnderIndAvg',
 'LowerLimitAge',
 'YearsToRetirement',
 'SEP']
SEP_NS SEP_SA SEP_SB SEP_SC SEP_SD SEP_SE SEP_SF SEP_SG SEP_SH SEP_SI SEP_SJ SEP_SK SEP_SL
0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 0 0 0 1 0 0 0 0 0 0 0 0 0
2 0 1 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 1 0 0 0 0 0 0 0 0
4 0 0 0 1 0 0 0 0 0 0 0 0 0
SALARY LOS SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement SEP SEP_NS SEP_SA SEP_SB SEP_SC SEP_SD SEP_SE SEP_SF SEP_SG SEP_SH SEP_SI SEP_SJ SEP_SK SEP_SL
0 63722.0 0.8 205.0 1319 64540.593830 -818.593830 25.0 32.0 SC 0 0 0 1 0 0 0 0 0 0 0 0 0
1 126245.0 4.8 207.0 1132 149864.298504 -23619.298504 30.0 27.0 SC 0 0 0 1 0 0 0 0 0 0 0 0 0
2 66585.0 4.9 439.0 1087 71530.963755 -4945.963755 50.0 7.0 SA 0 1 0 0 0 0 0 0 0 0 0 0 0
3 156737.0 39.8 670.0 265 146735.220304 10001.779696 55.0 2.0 SD 0 0 0 0 1 0 0 0 0 0 0 0 0
4 92973.0 1.0 721.0 1853 101641.124025 -8668.124025 50.0 7.0 SC 0 0 0 1 0 0 0 0 0 0 0 0 0
plotNumeric has 8170907 Records
CPU times: user 1 s, sys: 506 ms, total: 1.51 s
Wall time: 1.51 s
In [23]:
#%%time
#
#sns.set(font_scale=1)
#sns.pairplot(plotNumeric.drop(["SEP_NS", 
#                               "SEP_SA", 
#                               "SEP_SB", 
#                               "SEP_SC", 
#                               "SEP_SD", 
#                               "SEP_SE", 
#                               "SEP_SF", 
#                               "SEP_SG", 
#                               "SEP_SH", 
#                               "SEP_SI", 
#                               "SEP_SJ", 
#                               "SEP_SK", 
#                               "SEP_SL"
#                              ], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
In [24]:
%%time

# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()

def draw_histograms(df, variables, n_rows, n_cols):
    fig=plt.figure(figsize=(20,20))
    for i, var_name in enumerate(variables):
        ax=fig.add_subplot(n_rows,n_cols,i+1)
        df[var_name].hist(bins=20,ax=ax, color='#58D68D')
        ax.set_title(var_name+" Distribution")
    fig.tight_layout()  # Improves appearance a bit.
    plt.show()

draw_histograms(plotNumeric.drop(['SEP',
                                  "SEP_NS", 
                               "SEP_SA", 
                               "SEP_SB", 
                               "SEP_SC", 
                               "SEP_SD", 
                               "SEP_SE", 
                               "SEP_SF", 
                               "SEP_SG", 
                               "SEP_SH", 
                               "SEP_SI", 
                               "SEP_SJ", 
                               "SEP_SK", 
                               "SEP_SL"
                              ], axis=1),
                plotNumeric.drop(['SEP',
                                  "SEP_NS", 
                               "SEP_SA", 
                               "SEP_SB", 
                               "SEP_SC", 
                               "SEP_SD", 
                               "SEP_SE", 
                               "SEP_SF", 
                               "SEP_SG", 
                               "SEP_SH", 
                               "SEP_SI", 
                               "SEP_SJ", 
                               "SEP_SK", 
                               "SEP_SL"
                              ], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 4.72 s, sys: 6.47 s, total: 11.2 s
Wall time: 4.42 s
In [25]:
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html

#plt.matshow(plotNumeric.corr())

sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()

# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
                       square=True, annot=True, linewidths=1,
                       cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)

sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 8.9 s, sys: 1.3 s, total: 10.2 s
Wall time: 9.17 s

Based on the distribution of attributes identified above, we have decided to take the log transform of several attributes.

  • Salary
  • LOS (augmented by a value of .00001 to adjust for the undefined result of log(0)
  • SEPCount_EFDATE_OCC
  • SEPCount_EFDATE_LOC
In [26]:
%%time

# Log Transform Columns Added
OPMDataMerged["SALARYLog"] = OPMDataMerged["SALARY"].apply(np.log)
#OPMDataMerged["LOSLog"] = (OPMDataMerged["LOS"] + .00001).apply(np.log)
OPMDataMerged["LOSSqrt"] = (OPMDataMerged["LOS"]).apply(np.sqrt)
OPMDataMerged["SEPCount_EFDATE_OCCLog"] = OPMDataMerged["SEPCount_EFDATE_OCC"].apply(np.log)
OPMDataMerged["SEPCount_EFDATE_LOCLog"] = OPMDataMerged["SEPCount_EFDATE_LOC"].apply(np.log)
OPMDataMerged["IndAvgSalaryLog"] = OPMDataMerged["IndAvgSalary"].apply(np.log)
CPU times: user 993 ms, sys: 121 ms, total: 1.11 s
Wall time: 1.1 s

We next review categorical data to improve our understanding of factor levels.

In [27]:
#%%time
#            "LOCTYPT",
#            "OCCTYP",
#            "OCCTYPT",
#            "PPTYP",
#            "PPTYPT",
#            "AGYTYP",
#            "OCCFAM",
#            "PPGROUP",
#            "PAYPLAN",
#            "TOATYP",
#            "WSTYP",
#            "AGYSUBT",
#            "AGELVL",
#            "LOSLVL",
#            "LOC",
#            "OCC",
#            "PATCO",
#            "SALLVL",
#            "TOA",
#            "WORKSCH"]
#
#for i in dropCols:
#    cols.remove(i)
#
#plotCat = SampledOPMData[cols]
#display(plotCat.head())
#print("plotCat Has {0} Records".format(len(plotCat)))
#print("Number of colums = ", len(cols))
In [28]:
%%time

cols = list(OPMDataMerged.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
            "LOCTYPT",
            "OCCTYP",
            "OCCTYPT",
            "PPTYP",
            "PPTYPT",
            "AGYTYP",
            "OCCFAM",
            "PPGROUP",
            "PAYPLAN",
            "TOATYP",
            "WSTYP",
            "AGYSUBT",
            "AGELVL",
            "LOSLVL",
            "LOC",
            "OCC",
            "PATCO",
            "SALLVL",
            "TOA",
            "WORKSCH"]

for i in dropCols:
    cols.remove(i)

plotCat = OPMDataMerged[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
AGYSUB SEP DATECODE GENDER GSEGRD PPGRD AGYTYPT AGY AGYT QTR AGELVLT LOSLVLT LOCT OCCFAMT OCCT PATCOT PPGROUPT PAYPLANT SALLVLT TOATYPT TOAT WSTYPT WORKSCHT
0 AA00 SC 201507 M 11 GS-11 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES 4 25-29 Less than 1 year 11-DISTRICT OF COLUMBIA 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional Standard GSEG Pay Plans GS-GENERAL SCHEDULE $60,000 - $69,999 Non-permanent 40-Excepted Service - Schedule A Full-time Full-time Nonseasonal
1 AA00 SC 201506 F 15 GS-15 Small Independent Agencies (less than 100 empl... AA AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES 3 30-34 3 - 4 years 11-DISTRICT OF COLUMBIA 09xx-LEGAL AND KINDRED 0905-GENERAL ATTORNEY Professional Standard GSEG Pay Plans GS-GENERAL SCHEDULE $120,000 - $129,999 Permanent 30-Excepted Service - Schedule A Full-time Full-time Nonseasonal
2 AF** SA 201503 M 11 GS-11 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE 2 50-54 3 - 4 years 48-TEXAS 22xx-INFORMATION TECHNOLOGY 2210-INFORMATION TECHNOLOGY MANAGEMENT Administrative Standard GSEG Pay Plans GS-GENERAL SCHEDULE $60,000 - $69,999 Permanent 10-Competitive Service - Career Full-time Full-time Nonseasonal
3 AF02 SD 201506 M 15 GS-15 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE 3 55-59 35 years or more 35-NEW MEXICO 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative Standard GSEG Pay Plans GS-GENERAL SCHEDULE $150,000 - $159,999 Permanent 10-Competitive Service - Career Full-time Full-time Nonseasonal
4 AF03 SC 201509 M 13 GS-13 Cabinet Level Agencies AF AF-DEPARTMENT OF THE AIR FORCE 4 50-54 1 - 2 years 06-CALIFORNIA 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM Administrative Standard GSEG Pay Plans GS-GENERAL SCHEDULE $90,000 - $99,999 Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
plotCat Has 8170907 Records
Number of colums =  23
CPU times: user 2.95 s, sys: 1.17 s, total: 4.12 s
Wall time: 4.11 s

AGYSUB

High seperation among following:

  • Veterans Health Administration (VATA)
  • Forest Service (AG11)

GENDER

Similar separation distributions among males and females, except more terminations due to contract expiration among males

GSEGRD

High termination due to expired appt/other among following:

  • 3
  • 4
  • 5

Bimodal Quit distribution with outlier spike at GSEGRD 9:

  • Distribution 1 from GSEGRD 3 to 8
  • Distribution 2 from GSEGRD 11 to 15

Individual transfers highest among levels 11, 12, 13

PPGRD

Majority of distribution resides in GS values per the GSEGRD observations described above.... Are other PPGRD values of any significance? What are corporate grades all about?

AGYT

Top three Agencies with separation:

  1. AR-Department of the Army
  2. AG-Department of Agriculture
  3. VA-Department of Veteran Affairs

High contract termination in:

  • AG-Department of Agriculture
  • IN-Department of the Interior

While Veteran Affairs and Army both have many quits and many retirees, the Army has significantly more individual transfers (on par with retirements)

QTR

Most contract terminations in 1st and 4th quarters

Retirement peaks in 2nd quarter

Number of quits increases from one quarter to the next

*Bear in mind these are quarters from single year only so time-sensitive trends may not be applicable*

AGELVLT

High termination due to expired appt/other among following:

  • B
  • C

Number of Quits peaks at AGELVL D

Individual transfer counts mostly trend with Quits

Retirement highest at following:

  • I
  • J
  • K

LOSLVLT

Highest Quit count for LOSLVL A (< 1 year service) which then declines for levels B and C before spiking again at level D (5-9 years service)

Same pattern is observed for contract terminations but without any significant spikes with longer service

Large individual transfer spike at LOSLVL D (5-9 years service)

Retirement starts at LOSLVL D but trends upward to J

LOCT

Contract terminations comprise most California terminations among top total separation states

East Coast locations may possibly have most individual transfers, the most being in Washington DC

OCCFAMT

03xx-General Admin, clerical, and office svcs highest separation by far but indicates both high number of Quits and Retirements

Many quits in 06xx-Medical

04xx-Natural Resources again indicates high number of contract terminations

01xx-Social Science has even number of Quits and retirements

OCCT

PATCOT

PAYPLANT

Results skewed by GS

TOAT

WORKSCHT

Should model full time only

In [29]:
def subCountPlot(att1, att2, thresh):
    counts = plotCat.groupby([att1, att2]).size().unstack(fill_value=0) # Get att1 sizes by att2
    counts = pd.concat([counts,counts.sum(axis=1)], axis=1) # Calculate total for each att1 value and append total as new column
    counts.rename(columns={0:"Total"}, inplace=True)
    top = counts[counts["Total"] > thresh].index.tolist() # Obtain att1 values where total surpasses threshold
    
    zoom = plotCat[plotCat[att1].isin(top)] # Subset data to only the top att1 values
    f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
    sns.countplot(y=att1, data=zoom, color="blue", ax=ax1); # Dark blue signifies zoomed data
    sns.countplot(y=att1, data=zoom, hue=att2, palette="hls", ax=ax2);
In [30]:
def percBarPlot(att1, att2, numColors):
    # Create count by att1 and att2
    counts = plotCat.groupby([att1, att2]).size().unstack(fill_value=0) # Get att1 sizes by att2
    counts = pd.concat([counts,counts.sum(axis=1)], axis=1) # Calculate total for each att1 value and append total as new column
    counts.rename(columns={0:"Total"}, inplace=True)
    #counts.drop('Total', axis=1).plot(kind='bar', stacked=True)
    
    # create cmap from sns color palette
    my_cmap = ListedColormap(sns.color_palette('hls', numColors).as_hex())

    # Create and plot percentage by att1 and att2
    nest1 = []
    for i in counts.values:
        nest2 = []
        for j in i:
            nest2.append(float(j/(i[len(i)-1:]))*100)
        nest1.append(nest2)
    perc = pd.DataFrame(nest1)
    perc = perc.set_index(counts.index.values)
    perc.columns = counts.columns
    perc.drop('Total', axis=1).plot(kind='bar', stacked=True, ylim=(0,100), figsize={13,6}, title=att1+' Percentage Plot', colormap=my_cmap)
In [31]:
temp = cols[:4] # for quick visualization debug only; may delete once complete
In [32]:
%%time

for i in cols:
    if i != 'SEP':
        plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
        f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
        sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
        sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
        
    if i == 'AGYSUB':
        subCountPlot(i, 'SEP', 10000)
    elif i == 'LOCT':
        subCountPlot(i, 'SEP', 4000)
    elif i == 'OCCT':
        subCountPlot(i, 'SEP', 2000)
    elif i == 'PPGRD':
        subCountPlot(i, 'SEP', 6000)
    elif i == 'AGYT':
        subCountPlot(i, 'SEP', 3000)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
CPU times: user 10min 5s, sys: 13.5 s, total: 10min 19s
Wall time: 10min 18s
<matplotlib.figure.Figure at 0x7f602cd15390>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f5fa85bf7f0>
<matplotlib.figure.Figure at 0x7f5f72cee780>
<matplotlib.figure.Figure at 0x7f5f716c5978>
<matplotlib.figure.Figure at 0x7f5f7158bd30>
<matplotlib.figure.Figure at 0x7f5f54718240>
<matplotlib.figure.Figure at 0x7f5f53283550>
<matplotlib.figure.Figure at 0x7f5f530fda20>
<matplotlib.figure.Figure at 0x7f5f501e9a58>
<matplotlib.figure.Figure at 0x7f5f4f6b4940>
<matplotlib.figure.Figure at 0x7f5f4f531f60>
<matplotlib.figure.Figure at 0x7f5f4f268940>
<matplotlib.figure.Figure at 0x7f5f4ef9f2e8>
<matplotlib.figure.Figure at 0x7f5f4d967a90>
<matplotlib.figure.Figure at 0x7f5f449789e8>
<matplotlib.figure.Figure at 0x7f5f4089e860>
<matplotlib.figure.Figure at 0x7f5f406587f0>
<matplotlib.figure.Figure at 0x7f5f403f2f28>
<matplotlib.figure.Figure at 0x7f5f1aafb3c8>
<matplotlib.figure.Figure at 0x7f5f1a7384e0>
<matplotlib.figure.Figure at 0x7f5f1a582ba8>
<matplotlib.figure.Figure at 0x7f5f1a10c0b8>
In [33]:
%%time

for i in cols:
    if i != 'SEP':
        percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
CPU times: user 1min 11s, sys: 4.03 s, total: 1min 15s
Wall time: 1min 15s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [34]:
percBarPlot('GSEGRD', 'SALLVLT', len(plotCat.SALLVLT.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [35]:
percBarPlot('PATCOT', 'SALLVLT', len(plotCat.SALLVLT.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [36]:
%%time

sns.set(style="whitegrid", palette="pastel", color_codes=True)

sns.violinplot(x="PATCOT", y="SALARY", hue="GENDER", data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'], split=True,
               inner="quart", palette={"M": "b", "F": "pink"})
sns.despine(left=True)
CPU times: user 22.9 s, sys: 1min 5s, total: 1min 28s
Wall time: 14.2 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [37]:
%%time

# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", hue="GENDER", data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'], split=True,
               inner="quart", palette={"M": "b", "F": "pink"})
sns.despine(left=True)
CPU times: user 17.4 s, sys: 39.6 s, total: 57 s
Wall time: 12.2 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [38]:
%%time

sns.factorplot(x="SEP", y="SALARY", hue="GENDER", col="PATCOT",
               data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'],
               kind="violin", split=True, aspect=.4, size=10);
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 35.6 s, sys: 17 s, total: 52.6 s
Wall time: 38.6 s
Out[38]:
<seaborn.axisgrid.FacetGrid at 0x7f5f54667048>
In [39]:
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=OPMDataMerged,
#               kind="violin", split=True, aspect=.4, size=10, palette = "hls");
In [40]:
#%%time
#
#g = sns.PairGrid(data=OPMDataMerged,
#                 x_vars=["SEP","PATCOT"],
#                 y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
#                 aspect=1, size=10)
#g.map(sns.violinplot, palette="pastel");
In [41]:
del(plotNumeric, plotCat)

Focusing in on our Target Demographic

After analyzing the above plots for our categorical data, we have decided to narrow our focus due to the large variability in the dataset. We take the below actions on our dataset:

  • Keep only Full-time Nonseasonal observations
  • Remove the location US-SUPPRESSED (SEE DATA DEFINITIONS) due to apparent bias towards unknowns in the non-separation data
  • Keep only General Schedele Grades above 7.
  • Focus model generation on White Collar Jobs only
  • Create a Training set for the Professional PATCO value, and a Testing set for Administration

In addition, we have opted to remove the below attributes for model generation:

  • Datecode, QTR; Although very relevant for merging data from alternate sources, we do not have several years of data so this does not bring us much value
  • All Agency Attributes(AGYTYP,AGYTYPT,AGY,AGYT,AGYSUB,AGYSUBT); We are not concerned with agencies
  • Gender; Missing values for Non-Separation observations
  • Count; Missing values for Non-Separation observations; Also, all values = 1 so not very useful
  • PAYPLAN,PAYPLANT,PPGRD; Much too granular than we care for
  • LOSLVL,LOSLVLT; we have a numerical version of this attribute
  • OCC,OCCT; Much too granular than we care for

Our goal is to limit our focus to Professional occupations, build a model, then test that generated model on the Administration segment of the population.

In [42]:
%%time
print(len(OPMDataMerged))
#Removing Attributes
cols = list(OPMDataMerged.columns)
dropCols = ["QTR",
            "AGYTYP",
            "AGYTYPT",
            "AGY",
            "AGYT",
            "AGYSUB",
            "AGYSUBT",
            "GENDER",
            "COUNT",
            "PAYPLAN",
            "PAYPLANT",
            "PPGRD",
            "LOSLVL",
            "LOSLVLT",
            "SALLVL",
            "SALLVLT",
            "OCC",
            "OCCT"]

for i in dropCols:
    if i in cols:
        cols.remove(i)

OPMDataMerged = OPMDataMerged[cols]

# Keep only Full-time Nonseasonal observations
OPMDataMerged = OPMDataMerged[OPMDataMerged["WORKSCH"] == "F"]

#Remove the location US-SUPPRESSED (SEE DATA DEFINITIONS)
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOC"] != "US"]

#Keep only General Schedele Grades above 7.
OPMDataMerged["GSEGRD"] = OPMDataMerged["GSEGRD"].apply(pd.to_numeric)
OPMDataMerged = OPMDataMerged[OPMDataMerged["GSEGRD"] >= 7]

#Focus model generation on White Collar Jobs only
OPMDataMerged = OPMDataMerged[OPMDataMerged["OCCTYP"] == "1"]

#Create a Training set for the Professional PATCO value, and a Testing set for Administration
OPMDataMergedProf = OPMDataMerged[OPMDataMerged["PATCO"] == "1"]
OPMDataMergedAdmin = OPMDataMerged[OPMDataMerged["PATCO"] == "2"]
8170907
CPU times: user 1min 55s, sys: 4.33 s, total: 1min 59s
Wall time: 1min 59s
In [43]:
display(OPMDataMergedProf.head())
print(len(OPMDataMergedProf))
SEP DATECODE AGELVL GSEGRD LOC PATCO TOA WORKSCH SALARY LOS AGELVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
0 SC 201507 C 11.0 11 1 40 F 63722.0 0.8 25-29 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 2 Non-permanent 40-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 205.0 1319 64540.593830 -818.593830 25.0 32.0 0.4 0.5 34 2.6 11 13 74 0.4 10 1.2 11.062285 0.894427 5.323010 7.184629 11.075050
1 SC 201506 D 15.0 11 1 30 F 126245.0 4.8 30-34 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 09 09xx-LEGAL AND KINDRED Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 30-Excepted Service - Schedule A 1 Full-time Full-time Nonseasonal 207.0 1132 149864.298504 -23619.298504 30.0 27.0 0.4 0.5 34 2.3 12 13 65 0.4 10 1.2 11.745980 2.190890 5.332719 7.031741 11.917485
8 SD 201509 I 14.0 06 1 10 F 135500.0 14.3 55-59 1 United States 06-CALIFORNIA 1 White Collar 08 08xx-ENGINEERING AND ARCHITECTURE Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 122.0 1853 125803.916312 9696.083688 55.0 2.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4 11.816727 3.781534 4.804021 7.524561 11.742480
11 SD 201503 J 14.0 08 1 10 F 128223.0 20.6 60-64 1 United States 08-COLORADO 1 White Collar 08 08xx-ENGINEERING AND ARCHITECTURE Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 92.0 329 126328.546349 1894.453651 60.0 -3.0 0.3 0.4 31 3.0 9 10 86 0.5 12 1.1 11.761526 4.538722 4.521789 5.796058 11.746641
14 SA 201508 H 13.0 06 1 10 F 111566.0 24.3 50-54 1 United States 06-CALIFORNIA 1 White Collar 08 08xx-ENGINEERING AND ARCHITECTURE Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 110.0 1606 105047.296509 6518.703491 50.0 7.0 0.5 0.6 41 2.3 14 17 67 0.3 10 1.5 11.622372 4.929503 4.700480 7.381502 11.562166
1283383
In [44]:
display(OPMDataMergedAdmin.head())
print(len(OPMDataMergedAdmin))
SEP DATECODE AGELVL GSEGRD LOC PATCO TOA WORKSCH SALARY LOS AGELVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
2 SA 201503 H 11.0 48 2 10 F 66585.0 4.9 50-54 1 United States 48-TEXAS 1 White Collar 22 22xx-INFORMATION TECHNOLOGY Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 439.0 1087 71530.963755 -4945.963755 50.0 7.0 0.3 0.4 31 3.0 9 10 86 0.5 12 1.1 11.106235 2.213594 6.084499 6.991177 11.177886
3 SD 201506 I 15.0 35 2 10 F 156737.0 39.8 55-59 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 670.0 265 146735.220304 10001.779696 55.0 2.0 0.4 0.5 34 2.3 12 13 65 0.4 10 1.2 11.962325 6.308724 6.507278 5.579730 11.896385
4 SC 201509 H 13.0 06 2 15 F 92973.0 1.0 50-54 1 United States 06-CALIFORNIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 721.0 1853 101641.124025 -8668.124025 50.0 7.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4 11.440064 1.000000 6.580639 7.524561 11.529203
5 SD 201509 I 13.0 35 2 10 F 102943.0 11.3 55-59 1 United States 35-NEW MEXICO 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 721.0 557 101641.124025 1301.875975 55.0 2.0 0.4 0.5 38 1.9 12 14 55 0.4 12 1.4 11.541931 3.361547 6.580639 6.322565 11.529203
10 SA 201502 F 11.0 35 2 15 F 70621.0 9.7 40-44 1 United States 35-NEW MEXICO 1 White Collar 22 22xx-INFORMATION TECHNOLOGY Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 390.0 169 71530.963755 -909.963755 40.0 17.0 0.3 0.4 26 3.2 8 10 91 0.3 8 1.0 11.165083 3.114482 5.966147 5.129899 11.177886
2131840

Sampling

In [45]:
#curious on stratum SEP counts for full remaining data
stratum = pd.DataFrame({'StratCount' : OPMDataMerged.groupby(["SEP"]).size()}).reset_index()

display(stratum)
SEP StratCount
0 NS 4101470
1 SA 17983
2 SB 257
3 SC 22021
4 SD 35304
5 SE 876
6 SF 1988
7 SG 788
8 SH 79
9 SI 2727
10 SJ 5926
11 SK 1575
12 SL 18
In [46]:
#Assess Stratum SEP Counts for Prof, for use in sampling
maxSize=7500
stratumProf = pd.DataFrame({'StratCount' : OPMDataMergedProf.groupby(["SEP"]).size()}).reset_index()

stratumProf.loc[stratumProf["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumProf.loc[stratumProf["StratCount"]<=maxSize,"StratCountSample"] = stratumProf["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]

display(stratumProf)
SEP StratCount StratCountSample
0 NS 1259283 7500.0
1 SA 5463 5463.0
2 SB 85 85.0
3 SC 7423 7423.0
4 SD 8881 7500.0
5 SE 167 167.0
6 SF 338 338.0
7 SG 90 90.0
8 SH 15 15.0
9 SI 631 631.0
10 SJ 645 645.0
11 SK 355 355.0
12 SL 7 7.0
In [47]:
#Assess Stratum SEP Counts for Admin, for use in sampling
maxSize=7500
stratumAdmin = pd.DataFrame({'StratCount' : OPMDataMergedAdmin.groupby(["SEP"]).size()}).reset_index()

stratumAdmin.loc[stratumAdmin["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumAdmin.loc[stratumAdmin["StratCount"]<=maxSize,"StratCountSample"] = stratumAdmin["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]

display(stratumAdmin)
SEP StratCount StratCountSample
0 NS 2087084 7500.0
1 SA 9252 7500.0
2 SB 145 145.0
3 SC 9156 7500.0
4 SD 19402 7500.0
5 SE 555 555.0
6 SF 995 995.0
7 SG 414 414.0
8 SH 39 39.0
9 SI 1196 1196.0
10 SJ 2771 2771.0
11 SK 820 820.0
12 SL 11 11.0
In [48]:
%%time
def aggStratPop(stratum, OPMDataMerged):
    AggStrat = []

    for i in range(0,len(stratum)):
        sep = stratum["SEP"].ix[i]
        StratCountSample = stratum["StratCountSample"].ix[i]
        print("Stratum Sample Size Calculations for SEP: {}".format(sep))   
        AggStrat.append(pd.DataFrame({'StratCount' : OPMDataMerged[OPMDataMerged["SEP"]==sep].groupby(["DATECODE", "AGELVL"]).size()}).reset_index())
        AggStrat[i]["SEP"] = sep
        AggStrat[i]["TotalCount"] = len(OPMDataMerged[OPMDataMerged["SEP"]==sep])
        AggStrat[i]["p"] = AggStrat[i]["StratCount"] / AggStrat[i]["TotalCount"]
        AggStrat[i]["StratCountSample"] = StratCountSample
        AggStrat[i]["StratSampleSize"] = round(AggStrat[i]["p"] * StratCountSample).apply(int)

        display(AggStrat[i].head())
        print("totalStratumSampleSize: ", AggStrat[i]["StratSampleSize"].sum())
        #print(len(AggStrat[i]))
    return AggStrat
CPU times: user 5 µs, sys: 0 ns, total: 5 µs
Wall time: 10 µs
In [49]:
def SampleStrata(stratum, OPMDataMerged, FileName):
    AggStrat = aggStratPop(stratum, OPMDataMerged)

    SampledOPMStratumDataList = []

    for i,StratSampleSize in enumerate(AggStrat):
        SampledOPMStratumData = []
        for j in range(0,len(StratSampleSize)):
            SEP = StratSampleSize["SEP"].ix[j]
            DATECODE = StratSampleSize["DATECODE"].ix[j]
            AGELVL = StratSampleSize["AGELVL"].ix[j]
            SampleSize = StratSampleSize["StratSampleSize"].ix[j]
            print(SEP, DATECODE, AGELVL, SampleSize)

            SampledOPMStratumDataList.append(OPMDataMerged[(OPMDataMerged["SEP"]==SEP) 
                                                    & (OPMDataMerged["DATECODE"]==DATECODE) 
                                                    & (OPMDataMerged["AGELVL"]==AGELVL)].sample(SampleSize,  random_state=SampleSize))
        SampledOPMStratumData.append(pd.concat(SampledOPMStratumDataList))
        clear_display()
    SampledOPMData = pd.concat(SampledOPMStratumData).reset_index()
    del SampledOPMData["index"]
    pickleObject(SampledOPMData, FileName)
    clear_display()

    return SampledOPMData

Using a seed value equal to each strata sample size, we take random samples according to the computed sizes above. We loop through each Separation Type's Aggregated Strata Sample Sizes; Identify all observations matching on Datecode, Separation Type, and AgeLevel; and finally sample those observations with the computed sample size.

In [50]:
%%time
##Prof Data Sampling
if os.path.isfile(PickleJarPath+"/SampledOPMDataProf.pkl"):
    print("Found the File! Loading Pickle Now!")
    SampledOPMDataProf = unpickleObject("SampledOPMDataProf")
else:
    SampledOPMDataProf= SampleStrata(stratumProf, OPMDataMergedProf, "SampledOPMDataProf")
Found the File! Loading Pickle Now!
CPU times: user 26.9 ms, sys: 2.99 ms, total: 29.9 ms
Wall time: 29.6 ms
In [51]:
%%time
print(len(SampledOPMDataProf))
display(SampledOPMDataProf.head())
display(pd.DataFrame({'StratCount' : SampledOPMDataProf.groupby(["SEP"]).size()}).reset_index())
30227
SEP DATECODE AGELVL GSEGRD LOC PATCO TOA WORKSCH SALARY LOS AGELVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
0 NS 201412 B 11.0 51 1 15 F 77658.0 1.5 20-24 1 United States 51-VIRGINIA 1 White Collar 12 12xx-COPYRIGHT, PATENT, AND TRADE-MARK Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 26.0 1133 78919.462629 -1261.462629 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 11.260070 1.224745 3.258097 7.032624 11.276183
1 NS 201412 B 9.0 11 1 15 F 52146.0 2.9 20-24 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 11 11xx-BUSINESS AND INDUSTRY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 274.0 1260 52460.545881 -314.545881 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.861803 1.702939 5.613128 7.138867 10.867817
2 NS 201412 B 9.0 53 1 15 F 57368.0 0.5 20-24 1 United States 53-WASHINGTON 1 White Collar 08 08xx-ENGINEERING AND ARCHITECTURE Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 7.0 555 58563.540541 -1195.540541 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.957242 0.707107 1.945910 6.318968 10.977868
3 NS 201412 B 7.0 25 1 15 F 42830.0 1.5 20-24 1 United States 25-MASSACHUSETTS 1 White Collar 05 05xx-ACCOUNTING AND BUDGET Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 82.0 304 43211.010135 -381.010135 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.664994 1.224745 4.406719 5.717028 10.673851
4 NS 201412 B 7.0 53 1 15 F 44615.0 2.5 20-24 1 United States 53-WASHINGTON 1 White Collar 08 08xx-ENGINEERING AND ARCHITECTURE Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 50.0 555 49854.574586 -5239.574586 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.705825 1.581139 3.912023 6.318968 10.816866
SEP StratCount
0 NS 7501
1 SA 5463
2 SB 85
3 SC 7423
4 SD 7507
5 SE 167
6 SF 338
7 SG 90
8 SH 15
9 SI 631
10 SJ 645
11 SK 355
12 SL 7
CPU times: user 55.3 ms, sys: 7.56 ms, total: 62.9 ms
Wall time: 54.6 ms
In [52]:
%%time
#### Analyze Missing Values
filtered_msnoData = msno.nullity_sort(msno.nullity_filter(SampledOPMDataProf, filter='bottom', n=15, p=0.999), sort='descending')
msno.matrix(filtered_msnoData)

del filtered_msnoData
/usr/local/es7/lib/python3.5/site-packages/matplotlib/axes/_base.py:2903: UserWarning: Attempting to set identical left==right results
in singular transformations; automatically expanding.
left=-0.5, right=-0.5
  'left=%s, right=%s') % (left, right))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 417 ms, sys: 333 ms, total: 749 ms
Wall time: 367 ms
In [53]:
%%time
##Admin Data Sampling
if os.path.isfile(PickleJarPath+"/SampledOPMDataAdmin.pkl"):
    print("Found the File! Loading Pickle Now!")
    SampledOPMDataAdmin = unpickleObject("SampledOPMDataAdmin")
else:
    SampledOPMDataAdmin= SampleStrata(stratumAdmin, OPMDataMergedAdmin, "SampledOPMDataAdmin")
Found the File! Loading Pickle Now!
CPU times: user 45.1 ms, sys: 73.3 ms, total: 118 ms
Wall time: 39.2 ms
In [54]:
%%time
print(len(SampledOPMDataAdmin))
display(SampledOPMDataAdmin.head())
display(pd.DataFrame({'StratCount' : SampledOPMDataAdmin.groupby(["SEP"]).size()}).reset_index())
36956
SEP DATECODE AGELVL GSEGRD LOC PATCO TOA WORKSCH SALARY LOS AGELVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
0 NS 201412 B 7.0 11 2 38 F 42631.0 0.1 20-24 1 United States 11-DISTRICT OF COLUMBIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 38-Excepted Service - Other 1 Full-time Full-time Nonseasonal 774.0 1260 44891.572603 -2260.572603 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.660337 0.316228 6.651572 7.138867 10.712005
1 NS 201412 B 7.0 51 2 15 F 42631.0 5.0 20-24 1 United States 51-VIRGINIA 1 White Collar 11 11xx-BUSINESS AND INDUSTRY Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 14.0 1133 45621.916667 -2990.916667 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.660337 2.236068 2.639057 7.032624 10.728144
2 NS 201412 B 9.0 51 2 10 F 48893.0 3.4 20-24 1 United States 51-VIRGINIA 1 White Collar 03 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 774.0 1133 57175.047502 -8282.047502 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.797390 1.843909 6.651572 7.032624 10.953873
3 NS 201412 B 9.0 26 2 38 F 53828.0 1.7 20-24 1 United States 26-MICHIGAN 1 White Collar 21 21xx-TRANSPORTATION Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 38-Excepted Service - Other 1 Full-time Full-time Nonseasonal 80.0 214 56335.117117 -2507.117117 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.893549 1.303840 4.382027 5.365976 10.939073
4 NS 201412 B 7.0 34 2 48 F 41797.0 0.8 20-24 1 United States 34-NEW JERSEY 1 White Collar 00 00xx-MISCELLANEOUS OCCUPATIONS Administrative 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 2 Non-permanent 48-Excepted Service - Other 1 Full-time Full-time Nonseasonal 113.0 233 44956.060811 -3159.060811 20.0 37.0 0.5 0.4 30 2.2 12 10 62 0.3 7 1.1 10.640580 0.894427 4.727388 5.451038 10.713441
SEP StratCount
0 NS 7498
1 SA 7501
2 SB 145
3 SC 7507
4 SD 7504
5 SE 555
6 SF 995
7 SG 414
8 SH 39
9 SI 1196
10 SJ 2771
11 SK 820
12 SL 11
CPU times: user 51.4 ms, sys: 8.24 ms, total: 59.7 ms
Wall time: 46.4 ms
In [55]:
%%time
#### Analyze Missing Values
filtered_msnoData = msno.nullity_sort(msno.nullity_filter(SampledOPMDataAdmin, filter='bottom', n=15, p=0.999), sort='descending')
msno.matrix(filtered_msnoData)

del filtered_msnoData
/usr/local/es7/lib/python3.5/site-packages/matplotlib/axes/_base.py:2903: UserWarning: Attempting to set identical left==right results
in singular transformations; automatically expanding.
left=-0.5, right=-0.5
  'left=%s, right=%s') % (left, right))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 331 ms, sys: 234 ms, total: 566 ms
Wall time: 288 ms
In [56]:
%%time
## Describe Summary for our Model Professional Subgroup for Modeling
display(SampledOPMDataProf.describe().transpose())
count mean std min 25% 50% 75% max
GSEGRD 30227.0 12.142588 1.757041 7.000000 11.000000 12.000000 13.000000 15.000000
SALARY 30227.0 95587.756278 31018.478638 39179.000000 73143.000000 90711.000000 113673.000000 364782.000000
LOS 30227.0 14.333047 12.159974 0.000000 4.600000 9.700000 24.800000 71.500000
SEPCount_EFDATE_OCC 30227.0 146.208820 161.039507 1.000000 32.000000 82.000000 207.000000 708.000000
SEPCount_EFDATE_LOC 30227.0 737.081086 496.100839 18.000000 310.000000 596.000000 1123.000000 2791.000000
IndAvgSalary 30227.0 94981.435573 29622.413408 39179.000000 70499.260612 86256.027972 106119.623016 220806.750000
SalaryOverUnderIndAvg 30227.0 606.320705 9947.104620 -119176.750000 -5362.868080 227.033473 6789.125828 147896.670738
LowerLimitAge 30227.0 46.216462 12.931906 20.000000 35.000000 50.000000 60.000000 65.000000
YearsToRetirement 30227.0 10.783538 12.931906 -8.000000 -3.000000 7.000000 22.000000 37.000000
BLS_FEDERAL_OtherSep_Rate 30227.0 0.429315 0.084823 0.300000 0.400000 0.400000 0.500000 0.600000
BLS_FEDERAL_Quits_Rate 30227.0 0.455067 0.068862 0.300000 0.400000 0.500000 0.500000 0.600000
BLS_FEDERAL_TotalSep_Level 30227.0 36.292950 8.526718 26.000000 31.000000 34.000000 38.000000 60.000000
BLS_FEDERAL_JobOpenings_Rate 30227.0 2.450402 0.390278 1.900000 2.200000 2.300000 2.800000 3.200000
BLS_FEDERAL_OtherSep_Level 30227.0 11.951368 2.383434 8.000000 10.000000 12.000000 12.000000 17.000000
BLS_FEDERAL_Quits_Level 30227.0 12.004565 2.058074 9.000000 10.000000 13.000000 13.000000 17.000000
BLS_FEDERAL_JobOpenings_Level 30227.0 69.714196 11.375094 55.000000 62.000000 67.000000 80.000000 91.000000
BLS_FEDERAL_Layoffs_Rate 30227.0 0.461144 0.216841 0.300000 0.400000 0.400000 0.500000 1.100000
BLS_FEDERAL_Layoffs_Level 30227.0 12.412942 5.974603 7.000000 10.000000 12.000000 12.000000 30.000000
BLS_FEDERAL_TotalSep_Rate 30227.0 1.314652 0.316763 1.000000 1.100000 1.200000 1.400000 2.200000
SALARYLog 30227.0 11.417742 0.315717 10.575896 11.200172 11.415434 11.641081 12.807055
LOSSqrt 30227.0 3.392935 1.679622 0.000000 2.144761 3.114482 4.979960 8.455767
SEPCount_EFDATE_OCCLog 30227.0 4.297617 1.332665 0.000000 3.465736 4.406719 5.332719 6.562444
SEPCount_EFDATE_LOCLog 30227.0 6.319419 0.823629 2.890372 5.736572 6.390241 7.023759 7.934155
IndAvgSalaryLog 30227.0 11.416091 0.298912 10.575896 11.163358 11.365075 11.572322 12.305043
CPU times: user 98.5 ms, sys: 79.3 ms, total: 178 ms
Wall time: 71.4 ms
In [57]:
#%%time

#OPMDataMerged.to_csv("OPMDataMerged.csv")
In [58]:
#os.path.getsize("OPMDataMerged.csv") #Display file size in bytes

Review Visualizations post-Data removal and sampling

Chris... can you use the SampledOPMDataProf dataset, and re-run the Visuals?

In [59]:
%%time


cols = list(SampledOPMDataProf.select_dtypes(include=['float64', 'int64']))
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)

plotNumeric = SampledOPMDataProf[cols]

# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe

display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['GSEGRD',
 'SALARY',
 'LOS',
 'SEPCount_EFDATE_OCC',
 'SEPCount_EFDATE_LOC',
 'IndAvgSalary',
 'SalaryOverUnderIndAvg',
 'LowerLimitAge',
 'YearsToRetirement',
 'SALARYLog',
 'LOSSqrt',
 'SEPCount_EFDATE_OCCLog',
 'SEPCount_EFDATE_LOCLog',
 'IndAvgSalaryLog',
 'SEP']
SEP_NS SEP_SA SEP_SB SEP_SC SEP_SD SEP_SE SEP_SF SEP_SG SEP_SH SEP_SI SEP_SJ SEP_SK SEP_SL
0 1 0 0 0 0 0 0 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0 0 0 0 0 0 0
3 1 0 0 0 0 0 0 0 0 0 0 0 0
4 1 0 0 0 0 0 0 0 0 0 0 0 0
GSEGRD SALARY LOS SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog SEP SEP_NS SEP_SA SEP_SB SEP_SC SEP_SD SEP_SE SEP_SF SEP_SG SEP_SH SEP_SI SEP_SJ SEP_SK SEP_SL
0 11.0 77658.0 1.5 26.0 1133 78919.462629 -1261.462629 20.0 37.0 11.260070 1.224745 3.258097 7.032624 11.276183 NS 1 0 0 0 0 0 0 0 0 0 0 0 0
1 9.0 52146.0 2.9 274.0 1260 52460.545881 -314.545881 20.0 37.0 10.861803 1.702939 5.613128 7.138867 10.867817 NS 1 0 0 0 0 0 0 0 0 0 0 0 0
2 9.0 57368.0 0.5 7.0 555 58563.540541 -1195.540541 20.0 37.0 10.957242 0.707107 1.945910 6.318968 10.977868 NS 1 0 0 0 0 0 0 0 0 0 0 0 0
3 7.0 42830.0 1.5 82.0 304 43211.010135 -381.010135 20.0 37.0 10.664994 1.224745 4.406719 5.717028 10.673851 NS 1 0 0 0 0 0 0 0 0 0 0 0 0
4 7.0 44615.0 2.5 50.0 555 49854.574586 -5239.574586 20.0 37.0 10.705825 1.581139 3.912023 6.318968 10.816866 NS 1 0 0 0 0 0 0 0 0 0 0 0 0
plotNumeric has 30227 Records
CPU times: user 34.3 ms, sys: 2.56 ms, total: 36.9 ms
Wall time: 35.3 ms
In [60]:
%%time

sns.set(font_scale=1)
sns.pairplot(plotNumeric.drop(["SEP_NS", 
                               "SEP_SA", 
                               "SEP_SB", 
                               "SEP_SC", 
                               "SEP_SD", 
                               "SEP_SE", 
                               "SEP_SF", 
                               "SEP_SG", 
                               "SEP_SH", 
                               "SEP_SI", 
                               "SEP_SJ", 
                               "SEP_SK", 
                               "SEP_SL"
                              ], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1min 30s, sys: 1min 27s, total: 2min 57s
Wall time: 1min 13s
In [61]:
%%time

# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()

def draw_histograms(df, variables, n_rows, n_cols):
    fig=plt.figure(figsize=(20,20))
    for i, var_name in enumerate(variables):
        ax=fig.add_subplot(n_rows,n_cols,i+1)
        df[var_name].hist(bins=20,ax=ax, color='#58D68D')
        ax.set_title(var_name+" Distribution")
    fig.tight_layout()  # Improves appearance a bit.
    plt.show()

draw_histograms(plotNumeric.drop(['SEP',
                                  "SEP_NS", 
                               "SEP_SA", 
                               "SEP_SB", 
                               "SEP_SC", 
                               "SEP_SD", 
                               "SEP_SE", 
                               "SEP_SF", 
                               "SEP_SG", 
                               "SEP_SH", 
                               "SEP_SI", 
                               "SEP_SJ", 
                               "SEP_SK", 
                               "SEP_SL"
                              ], axis=1),
                plotNumeric.drop(['SEP',
                                  "SEP_NS", 
                               "SEP_SA", 
                               "SEP_SB", 
                               "SEP_SC", 
                               "SEP_SD", 
                               "SEP_SE", 
                               "SEP_SF", 
                               "SEP_SG", 
                               "SEP_SH", 
                               "SEP_SI", 
                               "SEP_SJ", 
                               "SEP_SK", 
                               "SEP_SL"
                              ], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 3.46 s, sys: 2.86 s, total: 6.33 s
Wall time: 2.95 s
In [62]:
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html

#plt.matshow(plotNumeric.corr())

sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()

# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
                       square=True, annot=True, linewidths=1,
                       cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)

sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 3.34 s, sys: 935 ms, total: 4.28 s
Wall time: 3.23 s
In [63]:
%%time

cols = list(SampledOPMDataProf.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
            "LOCTYPT",
            "OCCTYP",
            "OCCTYPT",
            "PPTYP",
            "PPTYPT",
            "AGYTYP",
            "OCCFAM",
            "PPGROUP",
            "PAYPLAN",
            "TOATYP",
            "WSTYP",
            "AGYSUBT",
            "AGELVL",
            "LOSLVL",
            "LOC",
            "OCC",
            "PATCO",
            "SALLVL",
            "TOA",
            "WORKSCH"]

for i in dropCols:
    if(i in list(SampledOPMDataProf.columns)): cols.remove(i)

plotCat = SampledOPMDataProf[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
SEP DATECODE AGELVLT LOCT OCCFAMT PATCOT PPGROUPT TOATYPT TOAT WSTYPT WORKSCHT
0 NS 201412 20-24 51-VIRGINIA 12xx-COPYRIGHT, PATENT, AND TRADE-MARK Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
1 NS 201412 20-24 11-DISTRICT OF COLUMBIA 11xx-BUSINESS AND INDUSTRY Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
2 NS 201412 20-24 53-WASHINGTON 08xx-ENGINEERING AND ARCHITECTURE Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
3 NS 201412 20-24 25-MASSACHUSETTS 05xx-ACCOUNTING AND BUDGET Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
4 NS 201412 20-24 53-WASHINGTON 08xx-ENGINEERING AND ARCHITECTURE Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
plotCat Has 30227 Records
Number of colums =  11
CPU times: user 24.6 ms, sys: 4.17 ms, total: 28.8 ms
Wall time: 25.3 ms
In [64]:
%%time

for i in cols:
    if i != 'SEP':
        plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
        f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
        sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
        sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
        
    if i == 'AGYSUB':
        subCountPlot(i, 'SEP', 10000)
    elif i == 'LOCT':
        subCountPlot(i, 'SEP', 1000)
    elif i == 'OCCT':
        subCountPlot(i, 'SEP', 2000)
    elif i == 'PPGRD':
        subCountPlot(i, 'SEP', 6000)
    elif i == 'AGYT':
        subCountPlot(i, 'SEP', 3000)
CPU times: user 5 s, sys: 38.8 ms, total: 5.03 s
Wall time: 5.01 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
<matplotlib.figure.Figure at 0x7f5f199edeb8>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f5f5931dd68>
<matplotlib.figure.Figure at 0x7f602caa9320>
<matplotlib.figure.Figure at 0x7f5f47fa6978>
<matplotlib.figure.Figure at 0x7f5f4a93bd68>
<matplotlib.figure.Figure at 0x7f5f40bdcb70>
<matplotlib.figure.Figure at 0x7f5f173c0be0>
<matplotlib.figure.Figure at 0x7f5f16e2ada0>
<matplotlib.figure.Figure at 0x7f5f16d84630>
<matplotlib.figure.Figure at 0x7f5f1a119400>
In [65]:
%%time

for i in cols:
    if i != 'SEP':
        percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
CPU times: user 2.44 s, sys: 26.8 ms, total: 2.47 s
Wall time: 2.45 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [66]:
%%time

sns.set(style="whitegrid", palette="pastel", color_codes=True)

sns.violinplot(x="PATCOT", y="SALARY", data=SampledOPMDataProf, split=True,
               inner="quart")
sns.despine(left=True)
CPU times: user 1.47 s, sys: 8.25 s, total: 9.72 s
Wall time: 232 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [67]:
%%time

# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", data=SampledOPMDataProf, split=True,
               inner="box", scale="area", cut=0)
sns.despine(left=True)
CPU times: user 330 ms, sys: 252 ms, total: 582 ms
Wall time: 292 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [68]:
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT",
#               data=SampledOPMDataProf,
#               kind="violin", split=True, aspect=.5, size=15);
In [69]:
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=SampledOPMDataProf,
#               kind="violin", split=True, aspect=.4, size=10);
In [70]:
%%time

g = sns.PairGrid(data=SampledOPMDataProf,
                 x_vars=["SEP","PATCOT"],
                 y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
                 aspect=1, size=10)
g.map(sns.violinplot, palette="pastel", inner="quart");
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 7.9 s, sys: 34.2 s, total: 42.1 s
Wall time: 2.53 s

There are several separation types we would like to either roll up, or remove altogether.

We have chosen to roll separation into two Binary Categories.

1) NS, Non-Separation comprised of: a) NS, Non-Separation b) SA,Transfer Out - Individual Transfer c) SB,Transfer Out - Mass Transfer d) SD,Retirement - Voluntary e) SE,Retirement - Early Out f) SF,Retirement - Disability g) SG,Retirement - Other

2) SC, Quit

In [71]:
SampledOPMDataProf = SampledOPMDataProf[((SampledOPMDataProf["SEP"] == "NS") | (SampledOPMDataProf["SEP"] == "SD") | (SampledOPMDataProf["SEP"] == "SE") | (SampledOPMDataProf["SEP"] == "SF") | (SampledOPMDataProf["SEP"] == "SH") | (SampledOPMDataProf["SEP"] == "SA") | (SampledOPMDataProf["SEP"] == "SB") | (SampledOPMDataProf["SEP"] == "SC"))]

SampledOPMDataProf.loc[(SampledOPMDataProf["SEP"] != "SC") , "SEP"]="NS"

SampledOPMDataAdmin = SampledOPMDataAdmin[((SampledOPMDataAdmin["SEP"] == "NS") | (SampledOPMDataAdmin["SEP"] == "SD") | (SampledOPMDataAdmin["SEP"] == "SE") | (SampledOPMDataAdmin["SEP"] == "SF") | (SampledOPMDataAdmin["SEP"] == "SH") | (SampledOPMDataAdmin["SEP"] == "SA") | (SampledOPMDataAdmin["SEP"] == "SB") | (SampledOPMDataAdmin["SEP"] == "SC"))]

SampledOPMDataAdmin.loc[(SampledOPMDataAdmin["SEP"] != "SC") , "SEP"]="NS"
In [72]:
#Assess Stratum SEP Counts for Prof, for use in sampling
maxSize=7500
stratumProf = pd.DataFrame({'StratCount' : SampledOPMDataProf.groupby(["SEP"]).size()}).reset_index()

stratumProf.loc[stratumProf["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumProf.loc[stratumProf["StratCount"]<=maxSize,"StratCountSample"] = stratumProf["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]

display(stratumProf)
SEP StratCount StratCountSample
0 NS 21076 7500.0
1 SC 7423 7423.0
In [73]:
#Assess Stratum SEP Counts for Admin, for use in sampling
maxSize=7500
stratumAdmin = pd.DataFrame({'StratCount' : SampledOPMDataAdmin.groupby(["SEP"]).size()}).reset_index()

stratumAdmin.loc[stratumAdmin["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumAdmin.loc[stratumAdmin["StratCount"]<=maxSize,"StratCountSample"] = stratumAdmin["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]

display(stratumAdmin)
SEP StratCount StratCountSample
0 NS 24237 7500.0
1 SC 7507 7500.0
In [74]:
SampledOPMDataProf= SampleStrata(stratumProf, SampledOPMDataProf, "SampledOPMDataProfBinary")
SampledOPMDataAdmin= SampleStrata(stratumProf, SampledOPMDataAdmin, "SampledOPMDataProfBinary")
Stratum Sample Size Calculations for SEP: NS
DATECODE AGELVL StratCount SEP TotalCount p StratCountSample StratSampleSize
0 201410 B 2 NS 21076 0.000095 7500.0 1
1 201410 C 57 NS 21076 0.002704 7500.0 20
2 201410 D 85 NS 21076 0.004033 7500.0 30
3 201410 E 73 NS 21076 0.003464 7500.0 26
4 201410 F 74 NS 21076 0.003511 7500.0 26
totalStratumSampleSize:  7497
Stratum Sample Size Calculations for SEP: SC
DATECODE AGELVL StratCount SEP TotalCount p StratCountSample StratSampleSize
0 201410 B 10 SC 7423 0.001347 7423.0 10
1 201410 C 92 SC 7423 0.012394 7423.0 92
2 201410 D 154 SC 7423 0.020746 7423.0 154
3 201410 E 118 SC 7423 0.015897 7423.0 118
4 201410 F 80 SC 7423 0.010777 7423.0 80
totalStratumSampleSize:  7423
NS 201410 B 1
NS 201410 C 20
NS 201410 D 30
NS 201410 E 26
NS 201410 F 26
NS 201410 G 27
NS 201410 H 25
NS 201410 I 66
NS 201410 J 77
NS 201410 K 68
NS 201411 B 1
NS 201411 C 25
NS 201411 D 42
NS 201411 E 35
NS 201411 F 34
NS 201411 G 26
NS 201411 H 34
NS 201411 I 59
NS 201411 J 56
NS 201411 K 42
NS 201412 B 5
NS 201412 C 53
NS 201412 D 99
NS 201412 E 95
NS 201412 F 90
NS 201412 G 99
NS 201412 H 134
NS 201412 I 192
NS 201412 J 200
NS 201412 K 169
NS 201501 B 1
NS 201501 C 12
NS 201501 D 27
NS 201501 E 30
NS 201501 F 19
NS 201501 G 26
NS 201501 H 32
NS 201501 I 182
NS 201501 J 218
NS 201501 K 190
NS 201502 B 1
NS 201502 C 16
NS 201502 D 23
NS 201502 E 18
NS 201502 F 17
NS 201502 G 16
NS 201502 H 27
NS 201502 I 51
NS 201502 J 67
NS 201502 K 49
NS 201503 B 6
NS 201503 C 57
NS 201503 D 117
NS 201503 E 108
NS 201503 F 105
NS 201503 G 118
NS 201503 H 140
NS 201503 I 142
NS 201503 J 133
NS 201503 K 81
NS 201504 C 14
NS 201504 D 27
NS 201504 E 29
NS 201504 F 25
NS 201504 G 20
NS 201504 H 31
NS 201504 I 54
NS 201504 J 67
NS 201504 K 64
NS 201505 B 1
NS 201505 C 23
NS 201505 D 41
NS 201505 E 39
NS 201505 F 35
NS 201505 G 37
NS 201505 H 40
NS 201505 I 105
NS 201505 J 112
NS 201505 K 93
NS 201506 B 8
NS 201506 C 59
NS 201506 D 114
NS 201506 E 107
NS 201506 F 96
NS 201506 G 102
NS 201506 H 137
NS 201506 I 159
NS 201506 J 149
NS 201506 K 94
NS 201507 B 1
NS 201507 C 18
NS 201507 D 29
NS 201507 E 33
NS 201507 F 23
NS 201507 G 21
NS 201507 H 28
NS 201507 I 80
NS 201507 J 89
NS 201507 K 78
NS 201508 B 0
NS 201508 C 18
NS 201508 D 34
NS 201508 E 22
NS 201508 F 21
NS 201508 G 22
NS 201508 H 30
NS 201508 I 54
NS 201508 J 70
NS 201508 K 50
NS 201509 B 7
NS 201509 C 57
NS 201509 D 121
NS 201509 E 111
NS 201509 F 99
NS 201509 G 110
NS 201509 H 134
NS 201509 I 151
NS 201509 J 130
NS 201509 K 89
SC 201410 B 10
SC 201410 C 92
SC 201410 D 154
SC 201410 E 118
SC 201410 F 80
SC 201410 G 61
SC 201410 H 64
SC 201410 I 48
SC 201410 J 24
SC 201410 K 11
SC 201411 B 6
SC 201411 C 72
SC 201411 D 90
SC 201411 E 85
SC 201411 F 70
SC 201411 G 55
SC 201411 H 61
SC 201411 I 40
SC 201411 J 17
SC 201411 K 7
SC 201412 B 3
SC 201412 C 63
SC 201412 D 103
SC 201412 E 81
SC 201412 F 66
SC 201412 G 45
SC 201412 H 47
SC 201412 I 43
SC 201412 J 12
SC 201412 K 14
SC 201501 B 4
SC 201501 C 106
SC 201501 D 132
SC 201501 E 122
SC 201501 F 95
SC 201501 G 71
SC 201501 H 72
SC 201501 I 42
SC 201501 J 23
SC 201501 K 9
SC 201502 B 6
SC 201502 C 63
SC 201502 D 98
SC 201502 E 79
SC 201502 F 71
SC 201502 G 49
SC 201502 H 50
SC 201502 I 44
SC 201502 J 16
SC 201502 K 12
SC 201503 B 9
SC 201503 C 60
SC 201503 D 101
SC 201503 E 91
SC 201503 F 52
SC 201503 G 56
SC 201503 H 38
SC 201503 I 38
SC 201503 J 21
SC 201503 K 11
SC 201504 B 11
SC 201504 C 86
SC 201504 D 104
SC 201504 E 72
SC 201504 F 58
SC 201504 G 58
SC 201504 H 56
SC 201504 I 36
SC 201504 J 13
SC 201504 K 12
SC 201505 B 19
SC 201505 C 140
SC 201505 D 162
SC 201505 E 105
SC 201505 F 105
SC 201505 G 72
SC 201505 H 66
SC 201505 I 56
SC 201505 J 28
SC 201505 K 11
SC 201506 B 16
SC 201506 C 109
SC 201506 D 139
SC 201506 E 126
SC 201506 F 84
SC 201506 G 76
SC 201506 H 68
SC 201506 I 45
SC 201506 J 21
SC 201506 K 16
SC 201507 B 24
SC 201507 C 139
SC 201507 D 141
SC 201507 E 120
SC 201507 F 103
SC 201507 G 71
SC 201507 H 57
SC 201507 I 56
SC 201507 J 29
SC 201507 K 15
SC 201508 B 17
SC 201508 C 105
SC 201508 D 164
SC 201508 E 140
SC 201508 F 91
SC 201508 G 82
SC 201508 H 65
SC 201508 I 48
SC 201508 J 15
SC 201508 K 6
SC 201509 B 12
SC 201509 C 102
SC 201509 D 160
SC 201509 E 97
SC 201509 F 87
SC 201509 G 69
SC 201509 H 66
SC 201509 I 45
SC 201509 J 29
SC 201509 K 15
Stratum Sample Size Calculations for SEP: NS
DATECODE AGELVL StratCount SEP TotalCount p StratCountSample StratSampleSize
0 201410 B 3 NS 24237 0.000124 7500.0 1
1 201410 C 57 NS 24237 0.002352 7500.0 18
2 201410 D 133 NS 24237 0.005487 7500.0 41
3 201410 E 98 NS 24237 0.004043 7500.0 30
4 201410 F 114 NS 24237 0.004704 7500.0 35
totalStratumSampleSize:  7495
Stratum Sample Size Calculations for SEP: SC
DATECODE AGELVL StratCount SEP TotalCount p StratCountSample StratSampleSize
0 201410 B 16 SC 7507 0.002131 7423.0 16
1 201410 C 77 SC 7507 0.010257 7423.0 76
2 201410 D 142 SC 7507 0.018916 7423.0 140
3 201410 E 110 SC 7507 0.014653 7423.0 109
4 201410 F 66 SC 7507 0.008792 7423.0 65
totalStratumSampleSize:  7423
NS 201410 B 1
NS 201410 C 18
NS 201410 D 41
NS 201410 E 30
NS 201410 F 35
NS 201410 G 42
NS 201410 H 53
NS 201410 I 75
NS 201410 J 66
NS 201410 K 36
NS 201411 B 2
NS 201411 C 19
NS 201411 D 42
NS 201411 E 39
NS 201411 F 46
NS 201411 G 45
NS 201411 H 52
NS 201411 I 70
NS 201411 J 48
NS 201411 K 31
NS 201412 B 2
NS 201412 C 32
NS 201412 D 71
NS 201412 E 79
NS 201412 F 86
NS 201412 G 127
NS 201412 H 174
NS 201412 I 207
NS 201412 J 189
NS 201412 K 128
NS 201501 B 1
NS 201501 C 14
NS 201501 D 24
NS 201501 E 30
NS 201501 F 35
NS 201501 G 35
NS 201501 H 51
NS 201501 I 189
NS 201501 J 191
NS 201501 K 121
NS 201502 B 2
NS 201502 C 18
NS 201502 D 28
NS 201502 E 25
NS 201502 F 27
NS 201502 G 36
NS 201502 H 37
NS 201502 I 58
NS 201502 J 52
NS 201502 K 30
NS 201503 B 2
NS 201503 C 38
NS 201503 D 88
NS 201503 E 92
NS 201503 F 91
NS 201503 G 132
NS 201503 H 173
NS 201503 I 156
NS 201503 J 110
NS 201503 K 61
NS 201504 B 1
NS 201504 C 13
NS 201504 D 30
NS 201504 E 28
NS 201504 F 33
NS 201504 G 31
NS 201504 H 45
NS 201504 I 71
NS 201504 J 61
NS 201504 K 39
NS 201505 B 0
NS 201505 C 21
NS 201505 D 50
NS 201505 E 43
NS 201505 F 40
NS 201505 G 47
NS 201505 H 69
NS 201505 I 112
NS 201505 J 93
NS 201505 K 63
NS 201506 B 2
NS 201506 C 35
NS 201506 D 79
NS 201506 E 86
NS 201506 F 90
NS 201506 G 125
NS 201506 H 163
NS 201506 I 179
NS 201506 J 121
NS 201506 K 68
NS 201507 B 0
NS 201507 C 12
NS 201507 D 42
NS 201507 E 31
NS 201507 F 33
NS 201507 G 42
NS 201507 H 67
NS 201507 I 101
NS 201507 J 85
NS 201507 K 50
NS 201508 B 1
NS 201508 C 11
NS 201508 D 33
NS 201508 E 33
NS 201508 F 26
NS 201508 G 40
NS 201508 H 53
NS 201508 I 67
NS 201508 J 56
NS 201508 K 33
NS 201509 B 3
NS 201509 C 40
NS 201509 D 89
NS 201509 E 103
NS 201509 F 98
NS 201509 G 138
NS 201509 H 176
NS 201509 I 176
NS 201509 J 126
NS 201509 K 59
SC 201410 B 16
SC 201410 C 76
SC 201410 D 140
SC 201410 E 109
SC 201410 F 65
SC 201410 G 91
SC 201410 H 88
SC 201410 I 46
SC 201410 J 25
SC 201410 K 8
SC 201411 B 7
SC 201411 C 66
SC 201411 D 94
SC 201411 E 90
SC 201411 F 80
SC 201411 G 75
SC 201411 H 70
SC 201411 I 38
SC 201411 J 11
SC 201411 K 7
SC 201412 B 2
SC 201412 C 59
SC 201412 D 92
SC 201412 E 67
SC 201412 F 56
SC 201412 G 61
SC 201412 H 65
SC 201412 I 39
SC 201412 J 18
SC 201412 K 6
SC 201501 B 5
SC 201501 C 60
SC 201501 D 123
SC 201501 E 114
SC 201501 F 96
SC 201501 G 79
SC 201501 H 78
SC 201501 I 58
SC 201501 J 21
SC 201501 K 6
SC 201502 B 11
SC 201502 C 60
SC 201502 D 105
SC 201502 E 73
SC 201502 F 72
SC 201502 G 69
SC 201502 H 74
SC 201502 I 47
SC 201502 J 17
SC 201502 K 6
SC 201503 B 6
SC 201503 C 72
SC 201503 D 114
SC 201503 E 96
SC 201503 F 52
SC 201503 G 69
SC 201503 H 86
SC 201503 I 47
SC 201503 J 17
SC 201503 K 7
SC 201504 B 11
SC 201504 C 65
SC 201504 D 127
SC 201504 E 85
SC 201504 F 92
SC 201504 G 71
SC 201504 H 77
SC 201504 I 38
SC 201504 J 16
SC 201504 K 3
SC 201505 B 20
SC 201505 C 87
SC 201505 D 142
SC 201505 E 124
SC 201505 F 89
SC 201505 G 87
SC 201505 H 92
SC 201505 I 57
SC 201505 J 28
SC 201505 K 13
SC 201506 B 22
SC 201506 C 69
SC 201506 D 117
SC 201506 E 97
SC 201506 F 92
SC 201506 G 75
SC 201506 H 64
SC 201506 I 49
SC 201506 J 12
SC 201506 K 9
SC 201507 B 20
SC 201507 C 97
SC 201507 D 141
SC 201507 E 119
SC 201507 F 110
SC 201507 G 96
SC 201507 H 82
SC 201507 I 37
SC 201507 J 21
SC 201507 K 7
SC 201508 B 25
SC 201508 C 100
SC 201508 D 135
SC 201508 E 112
SC 201508 F 92
SC 201508 G 93
SC 201508 H 74
SC 201508 I 44
SC 201508 J 15
SC 201508 K 5
SC 201509 B 11
SC 201509 C 65
SC 201509 D 128
SC 201509 E 117
SC 201509 F 100
SC 201509 G 94
SC 201509 H 73
SC 201509 I 48
SC 201509 J 16
SC 201509 K 9
In [ ]:
 

Review Visualizations post-Data removal and second round of sampling

In [75]:
%%time


cols = list(SampledOPMDataProf.select_dtypes(include=['float64', 'int64']))
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)

plotNumeric = SampledOPMDataProf[cols]

# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe

display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['GSEGRD',
 'SALARY',
 'LOS',
 'SEPCount_EFDATE_OCC',
 'SEPCount_EFDATE_LOC',
 'IndAvgSalary',
 'SalaryOverUnderIndAvg',
 'LowerLimitAge',
 'YearsToRetirement',
 'SALARYLog',
 'LOSSqrt',
 'SEPCount_EFDATE_OCCLog',
 'SEPCount_EFDATE_LOCLog',
 'IndAvgSalaryLog',
 'SEP']
SEP_NS SEP_SC
0 1 0
1 1 0
2 1 0
3 1 0
4 1 0
GSEGRD SALARY LOS SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog SEP SEP_NS SEP_SC
0 11.0 61857.0 4.7 336.0 470 65898.205859 -4041.205859 20.0 37.0 11.032581 2.167948 5.817111 6.152733 11.095866 NS 1 0
1 12.0 71813.0 7.2 336.0 513 81218.917413 -9405.917413 25.0 32.0 11.181821 2.683282 5.817111 6.240276 11.304903 NS 1 0
2 11.0 63091.0 4.0 336.0 923 65898.205859 -2807.205859 25.0 32.0 11.052333 2.000000 5.817111 6.827629 11.095866 NS 1 0
3 12.0 75621.0 5.8 75.0 923 82168.243394 -6547.243394 25.0 32.0 11.233489 2.408319 4.317488 6.827629 11.316524 NS 1 0
4 13.0 95919.0 2.0 63.0 923 121938.733696 -26019.733696 25.0 32.0 11.471259 1.414214 4.143135 6.827629 11.711274 NS 1 0
plotNumeric has 14920 Records
CPU times: user 33.3 ms, sys: 349 µs, total: 33.6 ms
Wall time: 31.2 ms
In [76]:
%%time

sns.set(font_scale=1)
sns.pairplot(plotNumeric.drop(['SEP_NS',
                               'SEP_SC'], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 43.8 s, sys: 40.3 s, total: 1min 24s
Wall time: 35.8 s
In [77]:
%%time

# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()

def draw_histograms(df, variables, n_rows, n_cols):
    fig=plt.figure(figsize=(20,20))
    for i, var_name in enumerate(variables):
        ax=fig.add_subplot(n_rows,n_cols,i+1)
        df[var_name].hist(bins=20,ax=ax, color='#58D68D')
        ax.set_title(var_name+" Distribution")
    fig.tight_layout()  # Improves appearance a bit.
    plt.show()

draw_histograms(plotNumeric.drop(['SEP',
                                  'SEP_NS',
                                  'SEP_SC'
                                 ], axis=1),
                plotNumeric.drop(['SEP',
                                  'SEP_NS',
                                  'SEP_SC'
                                 ], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 4.55 s, sys: 2.85 s, total: 7.4 s
Wall time: 4.05 s
In [78]:
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html

#plt.matshow(plotNumeric.corr())

sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()

# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
                       square=True, annot=True, linewidths=1,
                       cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)

sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1.62 s, sys: 901 ms, total: 2.52 s
Wall time: 1.49 s
In [79]:
%%time

cols = list(SampledOPMDataProf.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
            "LOCTYPT",
            "OCCTYP",
            "OCCTYPT",
            "PPTYP",
            "PPTYPT",
            "AGYTYP",
            "OCCFAM",
            "PPGROUP",
            "PAYPLAN",
            "TOATYP",
            "WSTYP",
            "AGYSUBT",
            "AGELVL",
            "LOSLVL",
            "LOC",
            "OCC",
            "PATCO",
            "SALLVL",
            "TOA",
            "WORKSCH"]

for i in dropCols:
    if(i in list(SampledOPMDataProf.columns)): cols.remove(i)

plotCat = SampledOPMDataProf[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
SEP DATECODE AGELVLT LOCT OCCFAMT PATCOT PPGROUPT TOATYPT TOAT WSTYPT WORKSCHT
0 NS 201410 20-24 42-PENNSYLVANIA 11xx-BUSINESS AND INDUSTRY Professional Standard GSEG Pay Plans Permanent 10-Competitive Service - Career Full-time Full-time Nonseasonal
1 NS 201410 25-29 49-UTAH 11xx-BUSINESS AND INDUSTRY Professional Standard GSEG Pay Plans Permanent 10-Competitive Service - Career Full-time Full-time Nonseasonal
2 NS 201410 25-29 24-MARYLAND 11xx-BUSINESS AND INDUSTRY Professional Standard GSEG Pay Plans Permanent 38-Excepted Service - Other Full-time Full-time Nonseasonal
3 NS 201410 25-29 24-MARYLAND 05xx-ACCOUNTING AND BUDGET Professional Standard GSEG Pay Plans Permanent 10-Competitive Service - Career Full-time Full-time Nonseasonal
4 NS 201410 25-29 24-MARYLAND 06xx-MEDICAL, HOSPITAL, DENTAL & PUB HEALTH Professional Standard GSEG Pay Plans Permanent 15-Competitive Service - Career-Conditional Full-time Full-time Nonseasonal
plotCat Has 14920 Records
Number of colums =  11
CPU times: user 17.6 ms, sys: 3.87 ms, total: 21.4 ms
Wall time: 18.8 ms
In [80]:
%%time

for i in cols:
    if i != 'SEP':
        plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
        f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
        sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
        sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
        
    if i == 'AGYSUB':
        subCountPlot(i, 'SEP', 10000)
    elif i == 'LOCT':
        subCountPlot(i, 'SEP', 1000)
    elif i == 'OCCT':
        subCountPlot(i, 'SEP', 2000)
    elif i == 'PPGRD':
        subCountPlot(i, 'SEP', 6000)
    elif i == 'AGYT':
        subCountPlot(i, 'SEP', 3000)
CPU times: user 2.1 s, sys: 8.1 ms, total: 2.11 s
Wall time: 2.09 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
<matplotlib.figure.Figure at 0x7f5f4d5349e8>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f5f033cd9e8>
<matplotlib.figure.Figure at 0x7f5f56673ef0>
<matplotlib.figure.Figure at 0x7f5f4395ca58>
<matplotlib.figure.Figure at 0x7f5f47d96710>
<matplotlib.figure.Figure at 0x7f5f43969f28>
<matplotlib.figure.Figure at 0x7f5f4f7380b8>
<matplotlib.figure.Figure at 0x7f5f03adcb00>
<matplotlib.figure.Figure at 0x7f5f4bdc5438>
<matplotlib.figure.Figure at 0x7f5f51074320>
In [81]:
%%time

for i in cols:
    if i != 'SEP':
        percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
CPU times: user 1.05 s, sys: 59.3 ms, total: 1.11 s
Wall time: 1.05 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [82]:
%%time

sns.set(style="whitegrid", palette="pastel", color_codes=True)

sns.violinplot(x="PATCOT", y="SALARY", data=SampledOPMDataProf, split=True,
               inner="quart")
sns.despine(left=True)
CPU times: user 1.4 s, sys: 7.23 s, total: 8.63 s
Wall time: 304 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [83]:
%%time

# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", data=SampledOPMDataProf, split=True,
               inner="box", scale="area", cut=0)
sns.despine(left=True)
CPU times: user 150 ms, sys: 121 ms, total: 271 ms
Wall time: 130 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [84]:
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT",
#               data=SampledOPMDataProf,
#               kind="violin", split=True, aspect=.5, size=15);
In [85]:
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=SampledOPMDataProf,
#               kind="violin", split=True, aspect=.4, size=10);
In [86]:
%%time

g = sns.PairGrid(data=SampledOPMDataProf,
                 x_vars=["SEP","PATCOT"],
                 y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
                 aspect=1, size=10)
g.map(sns.violinplot, palette="pastel", inner="quart");
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 5.15 s, sys: 25.1 s, total: 30.2 s
Wall time: 1.31 s

Encode Categorical Attributes, and Remove Description Columns for Analysis Prep

Now that we have the dataset sampled, we still have some legwork necessary to convert our categorical attributes into binary integer values. Below we walk through this process for the following Attributes:

  • AGELVL
  • LOC
  • SALLVL
  • TOA
  • OCCTYP
  • OCCFAM
  • PPTYP
  • PPGROUP
  • TOATYP

Once these attributes have been encoded and description columns removed, we end up with a total of 2446 attributes in our dataset for analysis in our model generation.

In [87]:
# Clean up old objects no longer needed, to clear up memory
process = psutil.Process(os.getpid())
print("Memory Usage before Cleanup: ", process.memory_info().rss)

if 'AGELVL' in dir():
    del AGELVL
if 'AggIndAvgSalary' in dir():
    del AggIndAvgSalary
if 'AggIndAvgSalary2' in dir():
    del AggIndAvgSalary2
if 'AggSEPCount_EFDATE_LOC' in dir():
    del AggSEPCount_EFDATE_LOC
if 'AggSEPCount_EFDATE_OCC' in dir():
    del AggSEPCount_EFDATE_OCC
if 'AggStrat' in dir():
    del AggStrat
if 'DATECODE' in dir():
    del DATECODE
if 'EMPColList' in dir():
    del EMPColList
if 'EMPDataOrig4Q' in dir():
    del EMPDataOrig4Q
if 'maxSize' in dir():
    del maxSize
if 'OPMColList' in dir():
    del OPMColList
if 'OPMDataFiles' in dir():
    del OPMDataFiles
if 'OPMDataList' in dir():
    del OPMDataList
if 'OPMDataMerged' in dir():
    del OPMDataMerged
if 'OPMDataOrig' in dir():
    del OPMDataOrig
if 'SEP' in dir():
    del SEP
if 'SampleSize' in dir():
    del SampleSize
if 'SampledOPMStratumData' in dir():
    del SampledOPMStratumData
if 'SampledOPMStratumDataList' in dir():
    del SampledOPMStratumDataList
if 'StratCountSample' in dir():
    del StratCountSample
if 'StratSampleSize' in dir():
    del StratSampleSize
if 'JTL' in dir():
    del JTL
    
process = psutil.Process(os.getpid())
print("Memory Usage after Cleanup: ", process.memory_info().rss)
Memory Usage before Cleanup:  14092587008
Memory Usage after Cleanup:  12382650368
In [88]:
display(SampledOPMDataProf.head())
SampledOPMDataProf.info()
SEP DATECODE AGELVL GSEGRD LOC PATCO TOA WORKSCH SALARY LOS AGELVLT LOCTYP LOCTYPT LOCT OCCTYP OCCTYPT OCCFAM OCCFAMT PATCOT PPTYP PPTYPT PPGROUP PPGROUPT TOATYP TOATYPT TOAT WSTYP WSTYPT WORKSCHT SEPCount_EFDATE_OCC SEPCount_EFDATE_LOC IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
0 NS 201410 B 11.0 42 1 10 F 61857.0 4.7 20-24 1 United States 42-PENNSYLVANIA 1 White Collar 11 11xx-BUSINESS AND INDUSTRY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 336.0 470 65898.205859 -4041.205859 20.0 37.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.032581 2.167948 5.817111 6.152733 11.095866
1 NS 201410 C 12.0 49 1 10 F 71813.0 7.2 25-29 1 United States 49-UTAH 1 White Collar 11 11xx-BUSINESS AND INDUSTRY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 336.0 513 81218.917413 -9405.917413 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.181821 2.683282 5.817111 6.240276 11.304903
2 NS 201410 C 11.0 24 1 38 F 63091.0 4.0 25-29 1 United States 24-MARYLAND 1 White Collar 11 11xx-BUSINESS AND INDUSTRY Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 38-Excepted Service - Other 1 Full-time Full-time Nonseasonal 336.0 923 65898.205859 -2807.205859 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.052333 2.000000 5.817111 6.827629 11.095866
3 NS 201410 C 12.0 24 1 10 F 75621.0 5.8 25-29 1 United States 24-MARYLAND 1 White Collar 05 05xx-ACCOUNTING AND BUDGET Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 10-Competitive Service - Career 1 Full-time Full-time Nonseasonal 75.0 923 82168.243394 -6547.243394 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.233489 2.408319 4.317488 6.827629 11.316524
4 NS 201410 C 13.0 24 1 15 F 95919.0 2.0 25-29 1 United States 24-MARYLAND 1 White Collar 06 06xx-MEDICAL, HOSPITAL, DENTAL & PUB HEALTH Professional 1 General Schedule and Equivalently Graded (GSEG... 11 Standard GSEG Pay Plans 1 Permanent 15-Competitive Service - Career-Conditional 1 Full-time Full-time Nonseasonal 63.0 923 121938.733696 -26019.733696 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.471259 1.414214 4.143135 6.827629 11.711274
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14920 entries, 0 to 14919
Data columns (total 50 columns):
SEP                              14920 non-null object
DATECODE                         14920 non-null object
AGELVL                           14920 non-null object
GSEGRD                           14920 non-null float64
LOC                              14920 non-null object
PATCO                            14920 non-null object
TOA                              14920 non-null object
WORKSCH                          14920 non-null object
SALARY                           14920 non-null float64
LOS                              14920 non-null float64
AGELVLT                          14920 non-null object
LOCTYP                           14920 non-null object
LOCTYPT                          14920 non-null object
LOCT                             14920 non-null object
OCCTYP                           14920 non-null object
OCCTYPT                          14920 non-null object
OCCFAM                           14920 non-null object
OCCFAMT                          14920 non-null object
PATCOT                           14920 non-null object
PPTYP                            14920 non-null object
PPTYPT                           14920 non-null object
PPGROUP                          14920 non-null object
PPGROUPT                         14920 non-null object
TOATYP                           14920 non-null object
TOATYPT                          14920 non-null object
TOAT                             14920 non-null object
WSTYP                            14920 non-null object
WSTYPT                           14920 non-null object
WORKSCHT                         14920 non-null object
SEPCount_EFDATE_OCC              14920 non-null float64
SEPCount_EFDATE_LOC              14920 non-null int64
IndAvgSalary                     14920 non-null float64
SalaryOverUnderIndAvg            14920 non-null float64
LowerLimitAge                    14920 non-null float64
YearsToRetirement                14920 non-null float64
BLS_FEDERAL_OtherSep_Rate        14920 non-null float64
BLS_FEDERAL_Quits_Rate           14920 non-null float64
BLS_FEDERAL_TotalSep_Level       14920 non-null int64
BLS_FEDERAL_JobOpenings_Rate     14920 non-null float64
BLS_FEDERAL_OtherSep_Level       14920 non-null int64
BLS_FEDERAL_Quits_Level          14920 non-null int64
BLS_FEDERAL_JobOpenings_Level    14920 non-null int64
BLS_FEDERAL_Layoffs_Rate         14920 non-null float64
BLS_FEDERAL_Layoffs_Level        14920 non-null int64
BLS_FEDERAL_TotalSep_Rate        14920 non-null float64
SALARYLog                        14920 non-null float64
LOSSqrt                          14920 non-null float64
SEPCount_EFDATE_OCCLog           14920 non-null float64
SEPCount_EFDATE_LOCLog           14920 non-null float64
IndAvgSalaryLog                  14920 non-null float64
dtypes: float64(18), int64(6), object(26)
memory usage: 5.7+ MB
In [89]:
%%time

if os.path.isfile(PickleJarPath+"/OPMAnalysisDataNoFamBinary.pkl"):
    print("Found the File! Loading Pickle Now!")
    OPMAnalysisDataNoFamBinary = unpickleObject("OPMAnalysisDataNoFamBinary")
else:

    OPMAnalysisDataNoFamBinary = SampledOPMDataProf.copy()

    cols = ["GENDER",
            "DATECODE",
            "QTR",
            "COUNT",
            "AGYTYPT",
            "AGYT",
            "AGYSUB",
            "AGYSUBT",
            "QTR",
            "AGELVLT",
            "LOSLVL",
            "LOSLVLT",
            "LOCTYPT",
            "LOCT",
            "OCCTYP",
            "OCCTYPT",
            "OCCFAM",
            "OCCFAMT",
            "OCC",
            "OCCT",
            "PATCO",
            "PPGRD",
            "PATCOT",
            "PPTYPT",
            "PPGROUPT",
            "PAYPLAN",
            "PAYPLANT",
            "SALLVLT",
            "TOATYPT",
            "TOAT",
            "WSTYP",
            "WSTYPT",
            "WORKSCH",
            "WORKSCHT",
            "SALARY",
            "LOS",
            "SEPCount_EFDATE_OCC",
            "SEPCount_EFDATE_LOC"
           ]



    #delete cols from analysis data
    for col in cols:
        if col in list(OPMAnalysisDataNoFamBinary.columns):
            del OPMAnalysisDataNoFamBinary[col]

    OPMAnalysisDataNoFamBinary.info()

    cols = ["AGELVL",
            "LOC",
            "SALLVL",
            "TOA",
            "AGYTYP",
            "AGY",
            "LOCTYP",
            "PPTYP",
            "PPGROUP",
            "TOATYP"
           ]

    #Split Values for cols 
    for col in cols:
        if col in list(OPMAnalysisDataNoFamBinary.columns):
            AttSplit = pd.get_dummies(OPMAnalysisDataNoFamBinary[col],prefix=col)
            display(AttSplit.head())
            OPMAnalysisDataNoFamBinary = pd.concat((OPMAnalysisDataNoFamBinary,AttSplit),axis=1) # add back into the dataframe
            del OPMAnalysisDataNoFamBinary[col]

    pickleObject(OPMAnalysisDataNoFamBinary, "OPMAnalysisDataNoFamBinary")
        
display(OPMAnalysisDataNoFamBinary.head())
print("Number of Columns: ",len(OPMAnalysisDataNoFamBinary.columns))
OPMAnalysisDataNoFamBinary.info()
Found the File! Loading Pickle Now!
SEP GSEGRD IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog AGELVL_B AGELVL_C AGELVL_D AGELVL_E AGELVL_F AGELVL_G AGELVL_H AGELVL_I AGELVL_J AGELVL_K LOC_01 LOC_02 LOC_04 LOC_05 LOC_06 LOC_08 LOC_09 LOC_10 LOC_11 LOC_12 LOC_13 LOC_15 LOC_16 LOC_17 LOC_18 LOC_19 LOC_20 LOC_21 LOC_22 LOC_23 LOC_24 LOC_25 LOC_26 LOC_27 LOC_28 LOC_29 LOC_30 LOC_31 LOC_32 LOC_33 LOC_34 LOC_35 LOC_36 LOC_37 LOC_38 LOC_39 LOC_40 LOC_41 LOC_42 LOC_44 LOC_45 LOC_46 LOC_47 LOC_48 LOC_49 LOC_50 LOC_51 LOC_53 LOC_54 LOC_55 LOC_56 TOA_10 TOA_15 TOA_20 TOA_30 TOA_32 TOA_35 TOA_38 TOA_40 TOA_42 TOA_44 TOA_45 TOA_48 LOCTYP_1 PPTYP_1 PPGROUP_11 PPGROUP_12 TOATYP_1 TOATYP_2
0 NS 11.0 65898.205859 -4041.205859 20.0 37.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.032581 2.167948 5.817111 6.152733 11.095866 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
1 NS 12.0 81218.917413 -9405.917413 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.181821 2.683282 5.817111 6.240276 11.304903 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
2 NS 11.0 65898.205859 -2807.205859 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.052333 2.000000 5.817111 6.827629 11.095866 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 0
3 NS 12.0 82168.243394 -6547.243394 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.233489 2.408319 4.317488 6.827629 11.316524 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
4 NS 13.0 121938.733696 -26019.733696 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.471259 1.414214 4.143135 6.827629 11.711274 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
Number of Columns:  100
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14920 entries, 0 to 14919
Data columns (total 100 columns):
SEP                              14920 non-null object
GSEGRD                           14920 non-null float64
IndAvgSalary                     14920 non-null float64
SalaryOverUnderIndAvg            14920 non-null float64
LowerLimitAge                    14920 non-null float64
YearsToRetirement                14920 non-null float64
BLS_FEDERAL_OtherSep_Rate        14920 non-null float64
BLS_FEDERAL_Quits_Rate           14920 non-null float64
BLS_FEDERAL_TotalSep_Level       14920 non-null int64
BLS_FEDERAL_JobOpenings_Rate     14920 non-null float64
BLS_FEDERAL_OtherSep_Level       14920 non-null int64
BLS_FEDERAL_Quits_Level          14920 non-null int64
BLS_FEDERAL_JobOpenings_Level    14920 non-null int64
BLS_FEDERAL_Layoffs_Rate         14920 non-null float64
BLS_FEDERAL_Layoffs_Level        14920 non-null int64
BLS_FEDERAL_TotalSep_Rate        14920 non-null float64
SALARYLog                        14920 non-null float64
LOSSqrt                          14920 non-null float64
SEPCount_EFDATE_OCCLog           14920 non-null float64
SEPCount_EFDATE_LOCLog           14920 non-null float64
IndAvgSalaryLog                  14920 non-null float64
AGELVL_B                         14920 non-null uint8
AGELVL_C                         14920 non-null uint8
AGELVL_D                         14920 non-null uint8
AGELVL_E                         14920 non-null uint8
AGELVL_F                         14920 non-null uint8
AGELVL_G                         14920 non-null uint8
AGELVL_H                         14920 non-null uint8
AGELVL_I                         14920 non-null uint8
AGELVL_J                         14920 non-null uint8
AGELVL_K                         14920 non-null uint8
LOC_01                           14920 non-null uint8
LOC_02                           14920 non-null uint8
LOC_04                           14920 non-null uint8
LOC_05                           14920 non-null uint8
LOC_06                           14920 non-null uint8
LOC_08                           14920 non-null uint8
LOC_09                           14920 non-null uint8
LOC_10                           14920 non-null uint8
LOC_11                           14920 non-null uint8
LOC_12                           14920 non-null uint8
LOC_13                           14920 non-null uint8
LOC_15                           14920 non-null uint8
LOC_16                           14920 non-null uint8
LOC_17                           14920 non-null uint8
LOC_18                           14920 non-null uint8
LOC_19                           14920 non-null uint8
LOC_20                           14920 non-null uint8
LOC_21                           14920 non-null uint8
LOC_22                           14920 non-null uint8
LOC_23                           14920 non-null uint8
LOC_24                           14920 non-null uint8
LOC_25                           14920 non-null uint8
LOC_26                           14920 non-null uint8
LOC_27                           14920 non-null uint8
LOC_28                           14920 non-null uint8
LOC_29                           14920 non-null uint8
LOC_30                           14920 non-null uint8
LOC_31                           14920 non-null uint8
LOC_32                           14920 non-null uint8
LOC_33                           14920 non-null uint8
LOC_34                           14920 non-null uint8
LOC_35                           14920 non-null uint8
LOC_36                           14920 non-null uint8
LOC_37                           14920 non-null uint8
LOC_38                           14920 non-null uint8
LOC_39                           14920 non-null uint8
LOC_40                           14920 non-null uint8
LOC_41                           14920 non-null uint8
LOC_42                           14920 non-null uint8
LOC_44                           14920 non-null uint8
LOC_45                           14920 non-null uint8
LOC_46                           14920 non-null uint8
LOC_47                           14920 non-null uint8
LOC_48                           14920 non-null uint8
LOC_49                           14920 non-null uint8
LOC_50                           14920 non-null uint8
LOC_51                           14920 non-null uint8
LOC_53                           14920 non-null uint8
LOC_54                           14920 non-null uint8
LOC_55                           14920 non-null uint8
LOC_56                           14920 non-null uint8
TOA_10                           14920 non-null uint8
TOA_15                           14920 non-null uint8
TOA_20                           14920 non-null uint8
TOA_30                           14920 non-null uint8
TOA_32                           14920 non-null uint8
TOA_35                           14920 non-null uint8
TOA_38                           14920 non-null uint8
TOA_40                           14920 non-null uint8
TOA_42                           14920 non-null uint8
TOA_44                           14920 non-null uint8
TOA_45                           14920 non-null uint8
TOA_48                           14920 non-null uint8
LOCTYP_1                         14920 non-null uint8
PPTYP_1                          14920 non-null uint8
PPGROUP_11                       14920 non-null uint8
PPGROUP_12                       14920 non-null uint8
TOATYP_1                         14920 non-null uint8
TOATYP_2                         14920 non-null uint8
dtypes: float64(15), int64(5), object(1), uint8(79)
memory usage: 3.5+ MB
CPU times: user 98.3 ms, sys: 3.58 ms, total: 102 ms
Wall time: 98.1 ms

Below is a display of all remaining attributes and their corresponding data types for analysis

In [90]:
%%time

data_type = []
for idx, col in enumerate(OPMAnalysisDataNoFamBinary.columns):
    data_type.append(OPMAnalysisDataNoFamBinary.dtypes[idx])

summary_df = {'Attribute Name' : pd.Series(OPMAnalysisDataNoFamBinary.columns, index = range(len(OPMAnalysisDataNoFamBinary.columns))), 'Data Type' : pd.Series(data_type, index = range(len(OPMAnalysisDataNoFamBinary.columns)))}
summary_df = pd.DataFrame(summary_df)
display(summary_df)

del data_type, summary_df
Attribute Name Data Type
0 SEP object
1 GSEGRD float64
2 IndAvgSalary float64
3 SalaryOverUnderIndAvg float64
4 LowerLimitAge float64
5 YearsToRetirement float64
6 BLS_FEDERAL_OtherSep_Rate float64
7 BLS_FEDERAL_Quits_Rate float64
8 BLS_FEDERAL_TotalSep_Level int64
9 BLS_FEDERAL_JobOpenings_Rate float64
10 BLS_FEDERAL_OtherSep_Level int64
11 BLS_FEDERAL_Quits_Level int64
12 BLS_FEDERAL_JobOpenings_Level int64
13 BLS_FEDERAL_Layoffs_Rate float64
14 BLS_FEDERAL_Layoffs_Level int64
15 BLS_FEDERAL_TotalSep_Rate float64
16 SALARYLog float64
17 LOSSqrt float64
18 SEPCount_EFDATE_OCCLog float64
19 SEPCount_EFDATE_LOCLog float64
20 IndAvgSalaryLog float64
21 AGELVL_B uint8
22 AGELVL_C uint8
23 AGELVL_D uint8
24 AGELVL_E uint8
25 AGELVL_F uint8
26 AGELVL_G uint8
27 AGELVL_H uint8
28 AGELVL_I uint8
29 AGELVL_J uint8
30 AGELVL_K uint8
31 LOC_01 uint8
32 LOC_02 uint8
33 LOC_04 uint8
34 LOC_05 uint8
35 LOC_06 uint8
36 LOC_08 uint8
37 LOC_09 uint8
38 LOC_10 uint8
39 LOC_11 uint8
40 LOC_12 uint8
41 LOC_13 uint8
42 LOC_15 uint8
43 LOC_16 uint8
44 LOC_17 uint8
45 LOC_18 uint8
46 LOC_19 uint8
47 LOC_20 uint8
48 LOC_21 uint8
49 LOC_22 uint8
50 LOC_23 uint8
51 LOC_24 uint8
52 LOC_25 uint8
53 LOC_26 uint8
54 LOC_27 uint8
55 LOC_28 uint8
56 LOC_29 uint8
57 LOC_30 uint8
58 LOC_31 uint8
59 LOC_32 uint8
60 LOC_33 uint8
61 LOC_34 uint8
62 LOC_35 uint8
63 LOC_36 uint8
64 LOC_37 uint8
65 LOC_38 uint8
66 LOC_39 uint8
67 LOC_40 uint8
68 LOC_41 uint8
69 LOC_42 uint8
70 LOC_44 uint8
71 LOC_45 uint8
72 LOC_46 uint8
73 LOC_47 uint8
74 LOC_48 uint8
75 LOC_49 uint8
76 LOC_50 uint8
77 LOC_51 uint8
78 LOC_53 uint8
79 LOC_54 uint8
80 LOC_55 uint8
81 LOC_56 uint8
82 TOA_10 uint8
83 TOA_15 uint8
84 TOA_20 uint8
85 TOA_30 uint8
86 TOA_32 uint8
87 TOA_35 uint8
88 TOA_38 uint8
89 TOA_40 uint8
90 TOA_42 uint8
91 TOA_44 uint8
92 TOA_45 uint8
93 TOA_48 uint8
94 LOCTYP_1 uint8
95 PPTYP_1 uint8
96 PPGROUP_11 uint8
97 PPGROUP_12 uint8
98 TOATYP_1 uint8
99 TOATYP_2 uint8
CPU times: user 24.9 ms, sys: 296 µs, total: 25.2 ms
Wall time: 24.1 ms

Dimensionality Reduction using Principal Component Analysis

We also scale the data values to remove bias in our models due to different attribute scales. Without scaling the data, attributes such as SALARY and LOS would carry heavier weights when compared against the binary encoded attributes and BLS data. This would cause unbalanced and improperly analyzed data for model creation.

In [91]:
OPMScaledAnalysisData = OPMAnalysisDataNoFamBinary.copy()
del OPMScaledAnalysisData["SEP"]
In [92]:
%%time

OPMAnalysisScalerFit = MinMaxScaler().fit(OPMScaledAnalysisData)
## Pickle for later re-use if needed
pickleObject(OPMAnalysisScalerFit, "OPMAnalysisScalerFit")

OPMScaledAnalysisData = pd.DataFrame(OPMAnalysisScalerFit.transform(OPMScaledAnalysisData), columns = OPMScaledAnalysisData.columns)
CPU times: user 13.3 ms, sys: 3.11 ms, total: 16.4 ms
Wall time: 17.3 ms
In [93]:
display(OPMScaledAnalysisData.head())
GSEGRD IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog AGELVL_B AGELVL_C AGELVL_D AGELVL_E AGELVL_F AGELVL_G AGELVL_H AGELVL_I AGELVL_J AGELVL_K LOC_01 LOC_02 LOC_04 LOC_05 LOC_06 LOC_08 LOC_09 LOC_10 LOC_11 LOC_12 LOC_13 LOC_15 LOC_16 LOC_17 LOC_18 LOC_19 LOC_20 LOC_21 LOC_22 LOC_23 LOC_24 LOC_25 LOC_26 LOC_27 LOC_28 LOC_29 LOC_30 LOC_31 LOC_32 LOC_33 LOC_34 LOC_35 LOC_36 LOC_37 LOC_38 LOC_39 LOC_40 LOC_41 LOC_42 LOC_44 LOC_45 LOC_46 LOC_47 LOC_48 LOC_49 LOC_50 LOC_51 LOC_53 LOC_54 LOC_55 LOC_56 TOA_10 TOA_15 TOA_20 TOA_30 TOA_32 TOA_35 TOA_38 TOA_40 TOA_42 TOA_44 TOA_45 TOA_48 LOCTYP_1 PPTYP_1 PPGROUP_11 PPGROUP_12 TOATYP_1 TOATYP_2
0 0.500 0.145270 0.471501 0.000000 1.000000 0.333333 0.333333 0.235294 0.153846 0.222222 0.25 0.083333 0.25 0.26087 0.166667 0.220405 0.256387 0.886424 0.646808 0.296670 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
1 0.625 0.229804 0.449531 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.25 0.083333 0.25 0.26087 0.166667 0.292431 0.317332 0.886424 0.664165 0.418258 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
2 0.500 0.145270 0.476554 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.25 0.083333 0.25 0.26087 0.166667 0.229938 0.236525 0.886424 0.780616 0.296670 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
3 0.625 0.235042 0.461238 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.25 0.083333 0.25 0.26087 0.166667 0.317367 0.284814 0.657909 0.780616 0.425018 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
4 0.750 0.454481 0.381495 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.25 0.083333 0.25 0.26087 0.166667 0.432120 0.167248 0.631340 0.780616 0.654628 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0

PCA Principal Components defined

Our objective, is to reduce dimensionality through identification of principal components. We have chosen to use the full column input (99) as the maximum number of components to be produced. Given our hopes are to reduce the number of attributes needed for a model, we expect to find much smaller than 99 as our Principal components which explain over 80% variance within the dataset. We will review each component's explained variance further to determine the proper number of components to be included later during model generation. Note randomized PCA was chosen in order to use singular value decomposition in our dimensionality reduction efforts due to the large size of our data set.

In [94]:
%%time

seed = len(OPMScaledAnalysisData)

print(OPMScaledAnalysisData.shape)
pca_class = PCA(n_components=len(OPMScaledAnalysisData.columns), svd_solver='randomized', random_state=seed)

pca_class.fit(OPMScaledAnalysisData)
(14920, 99)
CPU times: user 3.13 s, sys: 13.9 s, total: 17.1 s
Wall time: 398 ms

Below, the resulting components have been ordered by eigenvector value and these values portrayed as ratios of variance explained by each component. In order to identify the principal components to be included during model generation, we review the rate at which explained variance decreases in significance from one principal component to the next. Accompanying these proportion values is a scree plot representing these same values in visual form. By plotting the scree plot, it is easier to judge where this rate of decreasing explained variance occurs. Note the rate of change in explained variance among the first 8 principal components, with another less significant change through the 22th component. After the 22th component, the rate of decreasing explained variance begins to somewhat flatten out.

In [95]:
%%time

#The amount of variance that each PC explains
var= pca_class.explained_variance_ratio_

sns.set(font_scale=1.7)
plt.plot(range(1,len(OPMScaledAnalysisData.columns)+1), var*100, marker = '.', color = 'red', markerfacecolor = 'black')
plt.xlabel('Principal Components')
plt.ylabel('Percentage of Explained Variance')
plt.title('Scree Plot')
plt.axis([0, len(OPMScaledAnalysisData.columns)+1, -0.1, 9])
plt.annotate('22nd Component', xy=(22, 1.2), xytext=(40, 4),
                arrowprops=dict(facecolor='black', shrink=0.05),)
np.set_printoptions(suppress=True)
print(np.round(var, decimals=4)*100)
[ 11.45   9.85   6.05   5.42   4.49   3.84   3.61   3.41   3.13   2.99
   2.76   2.74   2.72   2.6    2.53   2.36   2.31   2.05   1.89   1.68
   1.64   1.18   1.09   0.96   0.93   0.91   0.81   0.78   0.68   0.66
   0.64   0.61   0.58   0.54   0.51   0.49   0.46   0.45   0.44   0.41
   0.39   0.39   0.36   0.36   0.3    0.3    0.29   0.28   0.28   0.28
   0.26   0.22   0.21   0.21   0.21   0.21   0.2    0.2    0.2    0.19
   0.18   0.17   0.17   0.16   0.13   0.13   0.11   0.11   0.1    0.1    0.1
   0.1    0.07   0.07   0.06   0.06   0.04   0.04   0.03   0.02   0.02
   0.02   0.01   0.01   0.     0.     0.     0.     0.     0.     0.     0.
   0.     0.     0.     0.     0.     0.     0.  ]
CPU times: user 287 ms, sys: 1.59 s, total: 1.88 s
Wall time: 68.6 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

By now referring to the cumulative variance values and associated plot below, it may be seen that the cumulative variance increases in a fairly consistent parabola curve. In attempts to acheive a cumulative variance explained of greater than 80%, we end at 22 principal components. For this reason, 22 principal components may be selected as being the most appropriate for separation classification modeling given the variables among these data.

In [96]:
#Cumulative Variance explains
var1=np.cumsum(np.round(pca_class.explained_variance_ratio_, decimals=4)*100)

plt.plot(range(1,len(OPMScaledAnalysisData.columns)+1), var1, marker = '.', color = 'green', markerfacecolor = 'black')
plt.xlabel('Principal Components')
plt.ylabel('Explained Variance (Sum %)')
plt.title('Cumulative Variance Plot')
plt.axis([0, len(OPMScaledAnalysisData.columns)+1, 10, len(OPMScaledAnalysisData.columns)+1])
plt.annotate('22nd Component', xy=(22, 80.54), xytext=(40, 60),
                arrowprops=dict(facecolor='black', shrink=0.05),)
print(var1)
[ 11.45  21.3   27.35  32.77  37.26  41.1   44.71  48.12  51.25  54.24  57.
  59.74  62.46  65.06  67.59  69.95  72.26  74.31  76.2   77.88  79.52
  80.7   81.79  82.75  83.68  84.59  85.4   86.18  86.86  87.52  88.16
  88.77  89.35  89.89  90.4   90.89  91.35  91.8   92.24  92.65  93.04
  93.43  93.79  94.15  94.45  94.75  95.04  95.32  95.6   95.88  96.14
  96.36  96.57  96.78  96.99  97.2   97.4   97.6   97.8   97.99  98.17
  98.34  98.51  98.67  98.8   98.93  99.04  99.15  99.25  99.35  99.45
  99.55  99.62  99.69  99.75  99.81  99.85  99.89  99.92  99.94  99.96
  99.98  99.99 100.   100.   100.   100.   100.   100.   100.   100.   100.
 100.   100.   100.   100.   100.   100.   100.  ]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

We proceed to analyze the first 4 component Feature Loadings more carefully. See below, plots of the top 10 loadings for each component.

In [97]:
plt.rcParams['figure.figsize'] = (20, 12)
fig = plt.figure()
plt.rcParams.update({'font.size': 16})
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15) 
for i in range(0,4):
    components = pd.Series(pca_class.components_[i], index=OPMScaledAnalysisData.columns)

    maxcomponent = pd.Series(pd.DataFrame(abs(components).sort_values(ascending=False).head(10)).index)

    matplotlib.rc('xtick', labelsize=12)


    ax = fig.add_subplot(2,2,i + 1)
       
    weightsplot = pd.Series(components, index=maxcomponent)
    weightsplot.plot(title = "Principal Component "+ str(i+1), kind='bar', color = 'Tomato', ax = ax)

plt.tight_layout()
plt.show()
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [98]:
MaxPC = 22

PCList = []
for i in range(0,MaxPC):
    components = pd.Series(pca_class.components_[i], index=OPMScaledAnalysisData.columns)

    maxcomponent = pd.Series(pd.DataFrame(abs(components).sort_values(ascending=False).head(15)).index)

    PCList.append(maxcomponent)

PCList = pd.concat(PCList).drop_duplicates().sort_values(ascending=True).reset_index(drop = True)
print(PCList)
PCList = list(PCList)
0                          AGELVL_C
1                          AGELVL_D
2                          AGELVL_E
3                          AGELVL_F
4                          AGELVL_G
5                          AGELVL_H
6                          AGELVL_I
7                          AGELVL_J
8                          AGELVL_K
9     BLS_FEDERAL_JobOpenings_Level
10     BLS_FEDERAL_JobOpenings_Rate
11        BLS_FEDERAL_Layoffs_Level
12         BLS_FEDERAL_Layoffs_Rate
13       BLS_FEDERAL_OtherSep_Level
14        BLS_FEDERAL_OtherSep_Rate
15          BLS_FEDERAL_Quits_Level
16           BLS_FEDERAL_Quits_Rate
17       BLS_FEDERAL_TotalSep_Level
18        BLS_FEDERAL_TotalSep_Rate
19                           GSEGRD
20                     IndAvgSalary
21                  IndAvgSalaryLog
22                           LOC_04
23                           LOC_06
24                           LOC_08
25                           LOC_11
26                           LOC_12
27                           LOC_13
28                           LOC_24
29                           LOC_36
30                           LOC_48
31                           LOC_51
32                          LOSSqrt
33                    LowerLimitAge
34                       PPGROUP_11
35                       PPGROUP_12
36                        SALARYLog
37           SEPCount_EFDATE_LOCLog
38           SEPCount_EFDATE_OCCLog
39                         TOATYP_1
40                         TOATYP_2
41                           TOA_10
42                           TOA_15
43                           TOA_20
44                           TOA_30
45                           TOA_38
46                           TOA_40
47                           TOA_48
48                YearsToRetirement
dtype: object

Total of 50 features of the original 99 are identified, by taking the top 15 feature loadings within the first 22 components as determined above as the appropriate components to maximize variance explained. We may now, optionally utilize these 50 features identified, or utilize principal component vectors for analysis in the next steps.

Separation Response Weights

Due to the unproportional number of observations in each separation type in our dataset, we need to create weightings. using SciKit's class_weight algorithm, we compute an array of weights to be used downstream in our models.

Predicting Separation

We have chosen to utilize Stratified KFold Cross Validation for our classification analysis, with 5 folds. This means, that from our original sample size of 16,638, each "fold" will save off approximately 20% as test observations utilizing the rest as training observations all while keeping the ratio of classes equal amongst customers and subscribers. This process will occur through 5 iterations, or folds, to allow us to cross validate our results amongst different test/train combinations. We have utilized a random_state seed equal to the length of the original sampled dataset to ensure reproducible results.

In [99]:
seed = len(OPMAnalysisDataNoFamBinary)

cv = StratifiedKFold(n_splits = 5, random_state = seed)
print(OPMAnalysisDataNoFamBinary.shape)
print(cv)
(14920, 100)
StratifiedKFold(n_splits=5, random_state=14920, shuffle=False)

Random Forest Classification

Max Depth The maximum depth (levels) in the tree. When a value is set, the tree may not split further once this level has been met regardless of how many nodes are in the leaf.

Max Features Number of features to consider when looking for a split.

Minimum Samples in Leaf Minimum number of samples required to be in a leaf node. Splits may not occur which cause the number of samples in a leaf to be less than this value. Too low a value here leads to overfitting the tree to train data.

Minimum Samples to Split Minimum number fo samples required to split a node. Care was taken during parameter tests to keep the ratio between Min Samples in Leaf and Min Samples to Split equal to that of the default values (1:2). This was done to allow an even 50/50 split on nodes which match the lowest granularity split criteria. similar to the min samples in leaf, too low a value here leads to overfitting the tree to train data.

n_estimators Number of Trees generated in the forest. Increasing the number of trees, in our models increased accuracy while decreasing performance. We tuned to provide output that completed all 10 iterations in under 10 minutes.

Not Complete#### After 13 iterations of modifying the above parameters, we land on a final winner based on the highest average Accuracy value across all iterations. Average Accuracy values in our 10 test/train iterations ranged from 70.2668 % from default inputs of the random forest classification model to a value of 72.5192 % in the best tuned model fit. Although the run-time of this model parameter choice is the largest performed, we decided to remain with these inputs due to the amount increase in accuracy. As mentioned previously, we tuned the n_estimators parameter to ensure we stayed under 10 minutes execution. Parameter inputs for the final Random Forest Classification model with the KD Tree Algorithm are as follows: ###Not Complete

max_depth max_features min_samples_leaf min_samples_split n_estimators
TBD TBD TBD TBD TBD
In [ ]:
 
In [100]:
%%time
"""
def rfc_explor(n_estimators,
               max_features,
               max_depth, 
               min_samples_split,
               min_samples_leaf,
               Data        = OPMAnalysisDataNoFam,
               cols        = PCList,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data[cols].as_matrix()
    
    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('CLF',rfc_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    print(TotalTime)
    print(accuracy)
    
    return accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 8.34 µs
Out[100]:
'\ndef rfc_explor(n_estimators,\n               max_features,\n               max_depth, \n               min_samples_split,\n               min_samples_leaf,\n               Data        = OPMAnalysisDataNoFam,\n               cols        = PCList,\n               cv          = cv,\n               seed        = seed):\n    startTime = datetime.now()\n    y = Data["SEP"].values # get the labels we want    \n    \n    X = Data[cols].as_matrix()\n    \n    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n    \n    # setup pipeline to take PCA, then fit a clf model\n    clf_pipe = Pipeline(\n        [(\'minMaxScaler\', MinMaxScaler()),\n         (\'CLF\',rfc_clf)]\n    )\n\n    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n    MeanAccuracy =  sum(accuracy)/len(accuracy)\n    accuracy = np.append(accuracy, MeanAccuracy)\n    endTime = datetime.now()\n    TotalTime = endTime - startTime\n    accuracy = np.append(accuracy, TotalTime)\n    \n    print(TotalTime)\n    print(accuracy)\n    \n    return accuracy\n'
In [101]:
%%time
"""
def rfc_explor_w_PCA(n_estimators,
               max_features,
               max_depth, 
               min_samples_split,
               min_samples_leaf,
               PCA,
               Data        = OPMAnalysisDataNoFam,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data.drop("SEP", axis=1).as_matrix()
    
    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('PCA', PCA),
         ('CLF',rfc_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 8.11 µs
Out[101]:
'\ndef rfc_explor_w_PCA(n_estimators,\n               max_features,\n               max_depth, \n               min_samples_split,\n               min_samples_leaf,\n               PCA,\n               Data        = OPMAnalysisDataNoFam,\n               cv          = cv,\n               seed        = seed):\n    startTime = datetime.now()\n    y = Data["SEP"].values # get the labels we want    \n    \n    X = Data.drop("SEP", axis=1).as_matrix()\n    \n    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n    \n    # setup pipeline to take PCA, then fit a clf model\n    clf_pipe = Pipeline(\n        [(\'minMaxScaler\', MinMaxScaler()),\n         (\'PCA\', PCA),\n         (\'CLF\',rfc_clf)]\n    )\n\n    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n    MeanAccuracy =  sum(accuracy)/len(accuracy)\n    accuracy = np.append(accuracy, MeanAccuracy)\n    endTime = datetime.now()\n    TotalTime = endTime - startTime\n    accuracy = np.append(accuracy, TotalTime)\n    \n    #print(TotalTime)\n    #print(accuracy)\n    \n    return accuracy\n'
In [102]:
%%time
"""
def rfc_explor_w_PCA(n_estimators,
               max_features,
               max_depth, 
               min_samples_split,
               min_samples_leaf,
               PCA,
               Data        = OPMAnalysisDataNoFam,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data.drop("SEP", axis=1).as_matrix()
    
    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('PCA', PCA),
         ('CLF',rfc_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
"""
CPU times: user 0 ns, sys: 4 µs, total: 4 µs
Wall time: 7.87 µs
Out[102]:
'\ndef rfc_explor_w_PCA(n_estimators,\n               max_features,\n               max_depth, \n               min_samples_split,\n               min_samples_leaf,\n               PCA,\n               Data        = OPMAnalysisDataNoFam,\n               cv          = cv,\n               seed        = seed):\n    startTime = datetime.now()\n    y = Data["SEP"].values # get the labels we want    \n    \n    X = Data.drop("SEP", axis=1).as_matrix()\n    \n    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n    \n    # setup pipeline to take PCA, then fit a clf model\n    clf_pipe = Pipeline(\n        [(\'minMaxScaler\', MinMaxScaler()),\n         (\'PCA\', PCA),\n         (\'CLF\',rfc_clf)]\n    )\n\n    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n    MeanAccuracy =  sum(accuracy)/len(accuracy)\n    accuracy = np.append(accuracy, MeanAccuracy)\n    endTime = datetime.now()\n    TotalTime = endTime - startTime\n    accuracy = np.append(accuracy, TotalTime)\n    \n    #print(TotalTime)\n    #print(accuracy)\n    \n    return accuracy\n'

We have created a function to be re-used for our cross-validation Accuracy Scores. Inputs of PCA components, Model CLF object, original sample data, and a CV containing our test/train splits allow us to easily produce an array of Accuracy Scores for the different permutations of models tested. A XXXXXXTBDXXXXX plot is also displayed depicting a view of the misclassification values for each iteration. Finally, a confusion matrix is displayed for the last test/train iteration for further interpretation on results.

In [103]:
%%time
"""
acclist = [] 
fullColumns = list(OPMAnalysisDataNoFam.columns)

for i in fullColumns:
    if i == "SEP": fullColumns.remove(i)

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14    , 14    , 14    , 14  , 14   , 14   ] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] 
min_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] 
min_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]

##Model with all Raw Scaled Features
for i in range(0,len(n_estimators)):
    acclist.append(rfc_explor(n_estimators      = n_estimators[i],
                              max_features      = max_features[i],
                              max_depth         = max_depth[i],
                              min_samples_split = min_samples_split[i],
                              min_samples_leaf  = min_samples_leaf[i],
                              cols              = fullColumns
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "All Raw Features",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
del rfcdf, acclist

acclist = []

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14    , 14    , 14    , 14  , 14   , 14   ] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] 
min_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] 
min_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]

## Model with only top 15 raw Scaled Principal Features 
for i in range(0,len(n_estimators)):
    acclist.append(rfc_explor(n_estimators      = n_estimators[i],
                              max_features      = max_features[i],
                              max_depth         = max_depth[i],
                              min_samples_split = min_samples_split[i],
                              min_samples_leaf  = min_samples_leaf[i]
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Top 15 Raw from PC",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
del rfcdf, acclist

### Model with PCA
acclist = []

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14    , 14    , 14    , 14  , 14   , 14   ] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] 
min_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] 
min_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]

for i in range(0,len(n_estimators)):
    acclist.append(rfc_explor_w_PCA(n_estimators      = n_estimators[i],
                                    max_features      = max_features[i],
                                    max_depth         = max_depth[i],
                                    min_samples_split = min_samples_split[i],
                                    min_samples_leaf  = min_samples_leaf[i],
                                    PCA               = PCA(n_components=22, svd_solver='randomized', random_state = seed)
                                   )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "With PCA",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)

#'Iteration 5', 'Iteration 6', 'Iteration 7', 'Iteration 8', 'Iteration 9', 
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 7.87 µs
Out[103]:
'\nacclist = [] \nfullColumns = list(OPMAnalysisDataNoFam.columns)\n\nfor i in fullColumns:\n    if i == "SEP": fullColumns.remove(i)\n\nn_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  \nmax_features       =  [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14    , 14    , 14    , 14  , 14   , 14   ] \nmax_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] \nmin_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] \nmin_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]\n\n##Model with all Raw Scaled Features\nfor i in range(0,len(n_estimators)):\n    acclist.append(rfc_explor(n_estimators      = n_estimators[i],\n                              max_features      = max_features[i],\n                              max_depth         = max_depth[i],\n                              min_samples_split = min_samples_split[i],\n                              min_samples_leaf  = min_samples_leaf[i],\n                              cols              = fullColumns\n                             )\n                  )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "All Raw Features",\n                                                "n_estimators": n_estimators,          \n                                                "max_features": max_features,         \n                                                "max_depth": max_depth,        \n                                                "min_samples_split": min_samples_split,\n                                                "min_samples_leaf": min_samples_leaf   \n                                              }),\n                               pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\ndel rfcdf, acclist\n\nacclist = []\n\nn_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  \nmax_features       =  [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14    , 14    , 14    , 14  , 14   , 14   ] \nmax_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] \nmin_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] \nmin_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]\n\n## Model with only top 15 raw Scaled Principal Features \nfor i in range(0,len(n_estimators)):\n    acclist.append(rfc_explor(n_estimators      = n_estimators[i],\n                              max_features      = max_features[i],\n                              max_depth         = max_depth[i],\n                              min_samples_split = min_samples_split[i],\n                              min_samples_leaf  = min_samples_leaf[i]\n                             )\n                  )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Top 15 Raw from PC",\n                                                "n_estimators": n_estimators,          \n                                                "max_features": max_features,         \n                                                "max_depth": max_depth,        \n                                                "min_samples_split": min_samples_split,\n                                                "min_samples_leaf": min_samples_leaf   \n                                              }),\n                               pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\ndel rfcdf, acclist\n\n### Model with PCA\nacclist = []\n\nn_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10  , 5    , 15   ]  \nmax_features       =  [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14    , 14    , 14    , 14  , 14   , 14   ] \nmax_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , 1000  , 500   , 100 , 1000 , 1000 ] \nmin_samples_split  =  [2     , 8      , 12    , 16    , 20    , 50    , 80    , 50    , 50    , 50    , 50  , 50   , 50   ] \nmin_samples_leaf   =  [1     , 4      , 6     , 8     , 10    , 25    , 40    , 25    , 25    , 25    , 25  , 25   , 25   ]\n\nfor i in range(0,len(n_estimators)):\n    acclist.append(rfc_explor_w_PCA(n_estimators      = n_estimators[i],\n                                    max_features      = max_features[i],\n                                    max_depth         = max_depth[i],\n                                    min_samples_split = min_samples_split[i],\n                                    min_samples_leaf  = min_samples_leaf[i],\n                                    PCA               = PCA(n_components=22, svd_solver=\'randomized\', random_state = seed)\n                                   )\n                  )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "With PCA",\n                                                "n_estimators": n_estimators,          \n                                                "max_features": max_features,         \n                                                "max_depth": max_depth,        \n                                                "min_samples_split": min_samples_split,\n                                                "min_samples_leaf": min_samples_leaf   \n                                              }),\n                               pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\n\n#\'Iteration 5\', \'Iteration 6\', \'Iteration 7\', \'Iteration 8\', \'Iteration 9\', \n'
In [104]:
"""
def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    
    #This function prints and plots the confusion matrix.
    #Normalization can be applied by setting `normalize=True`.
    
    plt.rcParams['figure.figsize'] = (18, 6)
    plt.rcParams.update({'font.size': 16})
    plt.rc('xtick', labelsize=18)
    plt.rc('ytick', labelsize=18) 
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize = 18)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, round(cm[i, j],2),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label', fontsize = 18)
    plt.xlabel('Predicted label', fontsize = 18)

    plt.show()
"""
Out[104]:
'\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title=\'Confusion matrix\',\n                          cmap=plt.cm.Blues):\n    \n    #This function prints and plots the confusion matrix.\n    #Normalization can be applied by setting `normalize=True`.\n    \n    plt.rcParams[\'figure.figsize\'] = (18, 6)\n    plt.rcParams.update({\'font.size\': 16})\n    plt.rc(\'xtick\', labelsize=18)\n    plt.rc(\'ytick\', labelsize=18) \n    plt.imshow(cm, interpolation=\'nearest\', cmap=cmap)\n    plt.title(title, fontsize = 18)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    if normalize:\n        cm = cm.astype(\'float\') / cm.sum(axis=1)[:, np.newaxis]\n        print("Normalized confusion matrix")\n    else:\n        print(\'Confusion matrix, without normalization\')\n\n    print(cm)\n\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, round(cm[i, j],2),\n                 horizontalalignment="center",\n                 color="white" if cm[i, j] > thresh else "black")\n\n    plt.tight_layout()\n    plt.ylabel(\'True label\', fontsize = 18)\n    plt.xlabel(\'Predicted label\', fontsize = 18)\n\n    plt.show()\n'
In [105]:
%%time
"""
def compute_kfold_scores_Classification( clf,
                                         Data     = OPMAnalysisDataNoFam,
                                         cols     = PCList,
                                         cv       = cv):

    y = Data["SEP"].values # get the labels we want    
    
    y = np.where(y == 'NS', 0, 
                 np.where(y == 'SA', 1,
                          np.where(y == 'SC', 2,
                                   np.where(y == 'SD', 3,
                                            np.where(y == 'SH', 4,
                                                     5
                                                    )
                                           )
                                  )
                         )
                )
    
    X = Data[cols].as_matrix()


    # Run classifier with cross-validation and plot ROC curves

    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('CLF',clf)]
    )
    
    
    accuracy = []
    #logloss = []
    
    for (train, test), color in zip(cv.split(X, y), colors):
        clf_pipe.fit(X[train],y[train])  # train object
        y_hat = clf_pipe.predict(X[test]) # get test set preditions
        
        a = float(mt.accuracy_score(y[test],y_hat))
        #l = float(mt.log_loss(y[test], y_hat))
        
        accuracy.append(round(a,5)) 

        #logloss.append(round(l,5)) 
    
    #print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))

    print("Accuracy Ratings across all iterations: {0}\n\n\
Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))

    
    ytestnames = np.where(y[test] ==  0,'NS', 
                          np.where(y[test] ==  1,'SA',
                                   np.where(y[test] ==  2,'SC',
                                            np.where(y[test] ==  3,'SD',
                                                     np.where(y[test] ==  4,'SH',
                                                              'SI'
                                                             )
                                                    )
                                           )
                                  )
                         )
    
    yhatnames  = np.where(y_hat ==  0,'NS', 
                          np.where(y_hat ==  1,'SA',
                                   np.where(y_hat ==  2,'SC',
                                            np.where(y_hat ==  3,'SD',
                                                     np.where(y_hat ==  4,'SH',
                                                              'SI'
                                                             )
                                                    )
                                           )
                                  )
                         )
    #print(set(list(y_hat)))
    print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))
        
        # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(confusion_matrix(y[test], y_hat), 
                          classes   =["NS",  "SA",   "SC", "SD",  "SI"], 
                          normalize =True,
                          title     ='Confusion matrix, with normalization')
    
    return clf_pipe.named_steps['CLF'], accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 7.87 µs
Out[105]:
'\ndef compute_kfold_scores_Classification( clf,\n                                         Data     = OPMAnalysisDataNoFam,\n                                         cols     = PCList,\n                                         cv       = cv):\n\n    y = Data["SEP"].values # get the labels we want    \n    \n    y = np.where(y == \'NS\', 0, \n                 np.where(y == \'SA\', 1,\n                          np.where(y == \'SC\', 2,\n                                   np.where(y == \'SD\', 3,\n                                            np.where(y == \'SH\', 4,\n                                                     5\n                                                    )\n                                           )\n                                  )\n                         )\n                )\n    \n    X = Data[cols].as_matrix()\n\n\n    # Run classifier with cross-validation and plot ROC curves\n\n    # setup pipeline to take PCA, then fit a clf model\n    clf_pipe = Pipeline(\n        [(\'minMaxScaler\', MinMaxScaler()),\n         (\'CLF\',clf)]\n    )\n    \n    \n    accuracy = []\n    #logloss = []\n    \n    for (train, test), color in zip(cv.split(X, y), colors):\n        clf_pipe.fit(X[train],y[train])  # train object\n        y_hat = clf_pipe.predict(X[test]) # get test set preditions\n        \n        a = float(mt.accuracy_score(y[test],y_hat))\n        #l = float(mt.log_loss(y[test], y_hat))\n        \n        accuracy.append(round(a,5)) \n\n        #logloss.append(round(l,5)) \n    \n    #print("Accuracy Ratings across all iterations: {0}\n\n#Average Accuracy: {1}\n\n#Log Loss Values across all iterations: {2}\n\n#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))\n\n    print("Accuracy Ratings across all iterations: {0}\n\nAverage Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))\n\n    \n    ytestnames = np.where(y[test] ==  0,\'NS\', \n                          np.where(y[test] ==  1,\'SA\',\n                                   np.where(y[test] ==  2,\'SC\',\n                                            np.where(y[test] ==  3,\'SD\',\n                                                     np.where(y[test] ==  4,\'SH\',\n                                                              \'SI\'\n                                                             )\n                                                    )\n                                           )\n                                  )\n                         )\n    \n    yhatnames  = np.where(y_hat ==  0,\'NS\', \n                          np.where(y_hat ==  1,\'SA\',\n                                   np.where(y_hat ==  2,\'SC\',\n                                            np.where(y_hat ==  3,\'SD\',\n                                                     np.where(y_hat ==  4,\'SH\',\n                                                              \'SI\'\n                                                             )\n                                                    )\n                                           )\n                                  )\n                         )\n    #print(set(list(y_hat)))\n    print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = [\'True\'], colnames = [\'Predicted\'], margins = True)))\n        \n        # Plot non-normalized confusion matrix\n    plt.figure()\n    plot_confusion_matrix(confusion_matrix(y[test], y_hat), \n                          classes   =["NS",  "SA",   "SC", "SD",  "SI"], \n                          normalize =True,\n                          title     =\'Confusion matrix, with normalization\')\n    \n    return clf_pipe.named_steps[\'CLF\'], accuracy\n'
In [106]:
%%time
"""
rfc_clf = RandomForestClassifier(n_estimators       = 15, 
                                 max_features       = 14, 
                                 max_depth          = 1000.0, 
                                 min_samples_split  = 50, 
                                 min_samples_leaf   = 25, 
                                 class_weight       = "balanced",
                                 n_jobs             = -1, 
                                 random_state       = seed) # get object
    
rfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)
"""
CPU times: user 10 µs, sys: 3 µs, total: 13 µs
Wall time: 17.6 µs
Out[106]:
'\nrfc_clf = RandomForestClassifier(n_estimators       = 15, \n                                 max_features       = 14, \n                                 max_depth          = 1000.0, \n                                 min_samples_split  = 50, \n                                 min_samples_leaf   = 25, \n                                 class_weight       = "balanced",\n                                 n_jobs             = -1, \n                                 random_state       = seed) # get object\n    \nrfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)\n'
In [107]:
"""
list(OPMAnalysisDataNoFam.SEP.unique())
"""
Out[107]:
'\nlist(OPMAnalysisDataNoFam.SEP.unique())\n'
**Experimenting with multiclass ROC curves below.**
In [108]:
'''%%time

def compute_kfold_scores_Classification( clf,
                                         Data     = OPMAnalysisDataNoFam,
                                         cols     = PCList,
                                         cv       = cv):

    y = Data["SEP"].values # get the labels we want    
    
    y = np.where(y == 'NS', 0, 
                 np.where(y == 'SA', 1,
                          np.where(y == 'SC', 2,
                                   np.where(y == 'SD', 3,
                                            np.where(y == 'SH', 4,
                                                     5
                                                    )
                                           )
                                  )
                         )
                )
    
    X = Data[cols].as_matrix()
    
    # Binarize the output
    y_bin = label_binarize(Data["SEP"].values, list(Data.SEP.unique()))
    n_classes = y_bin.shape[1]

    # Run classifier with cross-validation and plot ROC curves

    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('CLF',clf)]
    )
    
    colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])

    accuracy = []
    #logloss = []
    
    for (train, test), color in zip(cv.split(X, y), colors):
        clf_pipe.fit(X[train],y[train])  # train object
        y_hat = clf_pipe.predict(X[test]) # get test set preditions
        
        a = float(mt.accuracy_score(y[test],y_hat))
        #l = float(mt.log_loss(y[test], y_hat))
        
        accuracy.append(round(a,5)) 

        #logloss.append(round(l,5))
        
        # Compute ROC curve and area the curve
        #fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
        #mean_tpr += interp(mean_fpr, fpr, tpr)
        #mean_tpr[0] = 0.0
        #roc_auc = auc(fpr, tpr)
        #
        #plt.rcParams['figure.figsize'] = (12, 6)
        #
        #plt.plot(fpr, tpr, lw=lw, color=color,
        #         label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
#
        #i += 1
        # Compute ROC curve and ROC area for each class
        fpr = dict()
        tpr = dict()
        roc_auc = dict()
        for i in range(n_classes):
            fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
            roc_auc[i] = auc(fpr[i], tpr[i])

        # Plot of a ROC curve for a specific class
        plt.figure()
        lw = 2
        plt.plot(fpr[2], tpr[2], color='darkorange',
                 lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
        plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('Receiver operating characteristic example')
        plt.legend(loc="lower right")
        plt.show()
    #print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))

    print("Accuracy Ratings across all iterations: {0}\n\n\
Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))

    
    ytestnames = np.where(y[test] ==  0,'NS', 
                          np.where(y[test] ==  1,'SA',
                                   np.where(y[test] ==  2,'SC',
                                            np.where(y[test] ==  3,'SD',
                                                     np.where(y[test] ==  4,'SH',
                                                              'SI'
                                                             )
                                                    )
                                           )
                                  )
                         )
    
    yhatnames  = np.where(y_hat ==  0,'NS', 
                          np.where(y_hat ==  1,'SA',
                                   np.where(y_hat ==  2,'SC',
                                            np.where(y_hat ==  3,'SD',
                                                     np.where(y_hat ==  4,'SH',
                                                              'SI'
                                                             )
                                                    )
                                           )
                                  )
                         )
    #print(set(list(y_hat)))
    print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))
        
        # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(confusion_matrix(y[test], y_hat), 
                          classes   =["NS",  "SA",   "SC", "SD",  "SH",  "SI"], 
                          normalize =True,
                          title     ='Confusion matrix, with normalization')
    
    return clf_pipe.named_steps['CLF'], accuracy'''
Out[108]:
'%%time\n\ndef compute_kfold_scores_Classification( clf,\n                                         Data     = OPMAnalysisDataNoFam,\n                                         cols     = PCList,\n                                         cv       = cv):\n\n    y = Data["SEP"].values # get the labels we want    \n    \n    y = np.where(y == \'NS\', 0, \n                 np.where(y == \'SA\', 1,\n                          np.where(y == \'SC\', 2,\n                                   np.where(y == \'SD\', 3,\n                                            np.where(y == \'SH\', 4,\n                                                     5\n                                                    )\n                                           )\n                                  )\n                         )\n                )\n    \n    X = Data[cols].as_matrix()\n    \n    # Binarize the output\n    y_bin = label_binarize(Data["SEP"].values, list(Data.SEP.unique()))\n    n_classes = y_bin.shape[1]\n\n    # Run classifier with cross-validation and plot ROC curves\n\n    # setup pipeline to take PCA, then fit a clf model\n    clf_pipe = Pipeline(\n        [(\'minMaxScaler\', MinMaxScaler()),\n         (\'CLF\',clf)]\n    )\n    \n    colors = cycle([\'cyan\', \'indigo\', \'seagreen\', \'yellow\', \'blue\', \'darkorange\', \'pink\', \'darkred\', \'dimgray\', \'maroon\', \'coral\'])\n\n    accuracy = []\n    #logloss = []\n    \n    for (train, test), color in zip(cv.split(X, y), colors):\n        clf_pipe.fit(X[train],y[train])  # train object\n        y_hat = clf_pipe.predict(X[test]) # get test set preditions\n        \n        a = float(mt.accuracy_score(y[test],y_hat))\n        #l = float(mt.log_loss(y[test], y_hat))\n        \n        accuracy.append(round(a,5)) \n\n        #logloss.append(round(l,5))\n        \n        # Compute ROC curve and area the curve\n        #fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n        #mean_tpr += interp(mean_fpr, fpr, tpr)\n        #mean_tpr[0] = 0.0\n        #roc_auc = auc(fpr, tpr)\n        #\n        #plt.rcParams[\'figure.figsize\'] = (12, 6)\n        #\n        #plt.plot(fpr, tpr, lw=lw, color=color,\n        #         label=\'ROC fold %d (area = %0.2f)\' % (i, roc_auc))\n#\n        #i += 1\n        # Compute ROC curve and ROC area for each class\n        fpr = dict()\n        tpr = dict()\n        roc_auc = dict()\n        for i in range(n_classes):\n            fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n            roc_auc[i] = auc(fpr[i], tpr[i])\n\n        # Plot of a ROC curve for a specific class\n        plt.figure()\n        lw = 2\n        plt.plot(fpr[2], tpr[2], color=\'darkorange\',\n                 lw=lw, label=\'ROC curve (area = %0.2f)\' % roc_auc[2])\n        plt.plot([0, 1], [0, 1], color=\'navy\', lw=lw, linestyle=\'--\')\n        plt.xlim([0.0, 1.0])\n        plt.ylim([0.0, 1.05])\n        plt.xlabel(\'False Positive Rate\')\n        plt.ylabel(\'True Positive Rate\')\n        plt.title(\'Receiver operating characteristic example\')\n        plt.legend(loc="lower right")\n        plt.show()\n    #print("Accuracy Ratings across all iterations: {0}\n\n#Average Accuracy: {1}\n\n#Log Loss Values across all iterations: {2}\n\n#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))\n\n    print("Accuracy Ratings across all iterations: {0}\n\nAverage Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))\n\n    \n    ytestnames = np.where(y[test] ==  0,\'NS\', \n                          np.where(y[test] ==  1,\'SA\',\n                                   np.where(y[test] ==  2,\'SC\',\n                                            np.where(y[test] ==  3,\'SD\',\n                                                     np.where(y[test] ==  4,\'SH\',\n                                                              \'SI\'\n                                                             )\n                                                    )\n                                           )\n                                  )\n                         )\n    \n    yhatnames  = np.where(y_hat ==  0,\'NS\', \n                          np.where(y_hat ==  1,\'SA\',\n                                   np.where(y_hat ==  2,\'SC\',\n                                            np.where(y_hat ==  3,\'SD\',\n                                                     np.where(y_hat ==  4,\'SH\',\n                                                              \'SI\'\n                                                             )\n                                                    )\n                                           )\n                                  )\n                         )\n    #print(set(list(y_hat)))\n    print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = [\'True\'], colnames = [\'Predicted\'], margins = True)))\n        \n        # Plot non-normalized confusion matrix\n    plt.figure()\n    plot_confusion_matrix(confusion_matrix(y[test], y_hat), \n                          classes   =["NS",  "SA",   "SC", "SD",  "SH",  "SI"], \n                          normalize =True,\n                          title     =\'Confusion matrix, with normalization\')\n    \n    return clf_pipe.named_steps[\'CLF\'], accuracy'
In [109]:
'''%%time

rfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators       = 15, 
                                 max_features       = 14, 
                                 max_depth          = 1000.0, 
                                 min_samples_split  = 50, 
                                 min_samples_leaf   = 25, 
                                 class_weight       = "balanced",
                                 n_jobs             = -1, 
                                 random_state       = seed)) # get object
    
rfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)'''
Out[109]:
'%%time\n\nrfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators       = 15, \n                                 max_features       = 14, \n                                 max_depth          = 1000.0, \n                                 min_samples_split  = 50, \n                                 min_samples_leaf   = 25, \n                                 class_weight       = "balanced",\n                                 n_jobs             = -1, \n                                 random_state       = seed)) # get object\n    \nrfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)'

New attempt:

In [110]:
'''%%time

rfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators       = 15, 
                                 max_features       = 14, 
                                 max_depth          = 1000.0, 
                                 min_samples_split  = 50, 
                                 min_samples_leaf   = 25, 
                                 class_weight       = "balanced",
                                 n_jobs             = -1, 
                                 random_state       = seed)) # get object

y = OPMAnalysisDataNoFam["SEP"].values # get the labels we want
    
y = np.where(y == 'NS', 0, 
             np.where(y == 'SA', 1,
                      np.where(y == 'SC', 2,
                               np.where(y == 'SD', 3,
                                        np.where(y == 'SH', 4,
                                                 5
                                                )
                                       )
                              )
                     )
            )

X = OPMAnalysisDataNoFam[fullColumns].as_matrix()

# Binarize the output
#y_bin = label_binarize(OPMAnalysisDataNoFam["SEP"].values, list(OPMAnalysisDataNoFam.SEP.unique()))
n_classes = len(set(y))

#classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
#                                 random_state=random_state))
#y_score = rfc_clf.fit(X[train], y[train]).decision_function(X_test)
#
colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])
#
#accuracy = []
#

#for (train, test), color in zip(cv.split(X, y), colors):
#    probas_ = rfc_clf.fit(X[train], y[train]).predict_proba(X[test])    
#    
#    #rfc_clf.fit(X[train],y[train])  # train object
#    y_hat = rfc_clf.predict(X[test]) # get test set preditions
#    #y_hat = rfc_clf.fit(X[train],y[train]).decision_function(X[test])
#    
#    fpr = dict()
#    tpr = dict()
#    roc_auc = dict()
#    for i in range(n_classes):
#        #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
#        print(len(probas_[:, i]))
#        print(y[test])
#        #fpr[i], tpr[i], thresholds = roc_curve(y[test][:, i], probas_[:, i])
#        #roc_auc[i] = auc(fpr[i], tpr[i])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=seed)

probas_ = rfc_clf.fit(X_train, y_train).predict_proba(X_test)    
    
#rfc_clf.fit(X[train],y[train])  # train object
#y_hat = rfc_clf.predict(X_test) # get test set preditions
#y_hat = rfc_clf.fit(X_train,y_train).decision_function(X_test)

fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
    print(probas_[:, i])
    print(y_test)
    #fpr[i], tpr[i], thresholds = roc_curve(y[test][i], probas_[:, i])
    #roc_auc[i] = auc(fpr[i], tpr[i])'''
Out[110]:
'%%time\n\nrfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators       = 15, \n                                 max_features       = 14, \n                                 max_depth          = 1000.0, \n                                 min_samples_split  = 50, \n                                 min_samples_leaf   = 25, \n                                 class_weight       = "balanced",\n                                 n_jobs             = -1, \n                                 random_state       = seed)) # get object\n\ny = OPMAnalysisDataNoFam["SEP"].values # get the labels we want\n    \ny = np.where(y == \'NS\', 0, \n             np.where(y == \'SA\', 1,\n                      np.where(y == \'SC\', 2,\n                               np.where(y == \'SD\', 3,\n                                        np.where(y == \'SH\', 4,\n                                                 5\n                                                )\n                                       )\n                              )\n                     )\n            )\n\nX = OPMAnalysisDataNoFam[fullColumns].as_matrix()\n\n# Binarize the output\n#y_bin = label_binarize(OPMAnalysisDataNoFam["SEP"].values, list(OPMAnalysisDataNoFam.SEP.unique()))\nn_classes = len(set(y))\n\n#classifier = OneVsRestClassifier(svm.SVC(kernel=\'linear\', probability=True,\n#                                 random_state=random_state))\n#y_score = rfc_clf.fit(X[train], y[train]).decision_function(X_test)\n#\ncolors = cycle([\'cyan\', \'indigo\', \'seagreen\', \'yellow\', \'blue\', \'darkorange\', \'pink\', \'darkred\', \'dimgray\', \'maroon\', \'coral\'])\n#\n#accuracy = []\n#\n\n#for (train, test), color in zip(cv.split(X, y), colors):\n#    probas_ = rfc_clf.fit(X[train], y[train]).predict_proba(X[test])    \n#    \n#    #rfc_clf.fit(X[train],y[train])  # train object\n#    y_hat = rfc_clf.predict(X[test]) # get test set preditions\n#    #y_hat = rfc_clf.fit(X[train],y[train]).decision_function(X[test])\n#    \n#    fpr = dict()\n#    tpr = dict()\n#    roc_auc = dict()\n#    for i in range(n_classes):\n#        #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n#        print(len(probas_[:, i]))\n#        print(y[test])\n#        #fpr[i], tpr[i], thresholds = roc_curve(y[test][:, i], probas_[:, i])\n#        #roc_auc[i] = auc(fpr[i], tpr[i])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=seed)\n\nprobas_ = rfc_clf.fit(X_train, y_train).predict_proba(X_test)    \n    \n#rfc_clf.fit(X[train],y[train])  # train object\n#y_hat = rfc_clf.predict(X_test) # get test set preditions\n#y_hat = rfc_clf.fit(X_train,y_train).decision_function(X_test)\n\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(n_classes):\n    #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n    print(probas_[:, i])\n    print(y_test)\n    #fpr[i], tpr[i], thresholds = roc_curve(y[test][i], probas_[:, i])\n    #roc_auc[i] = auc(fpr[i], tpr[i])'
In [ ]:
 

Building a Binary Classification model

Reducing data to Non-Separation and Quit Separation Types

Separation Response Weights

As was done before, we assess weights across classes. Since stratification was performed previously, we have equal weights. Thus, we can ignore weighting in our binary classifications.

In [111]:
OPMClassWeights = class_weight.compute_class_weight("balanced", OPMAnalysisDataNoFamBinary["SEP"].drop_duplicates(), OPMAnalysisDataNoFamBinary["SEP"])

display(stratumProf.merge(pd.DataFrame({"Weight": OPMClassWeights, "SEP": OPMAnalysisDataNoFamBinary["SEP"].drop_duplicates()}),on="SEP", how="inner"))
SEP StratCount StratCountSample Weight
0 NS 21076 7500.0 0.995065
1 SC 7423 7423.0 1.004985

Predicting Separation

We have chosen to utilize Stratified KFold Cross Validation for our classification analysis, with 5 folds. This means, that from our original sample size of 8002, each "fold" will save off approximately 20% as test observations utilizing the rest as training observations all while keeping the ratio of classes equal amongst customers and subscribers. This process will occur through 5 iterations, or folds, to allow us to cross validate our results amongst different test/train combinations. We have utilized a random_state seed equal to the length of the original sampled dataset to ensure reproducible results.

In [112]:
seed = len(OPMAnalysisDataNoFamBinary)

cv = StratifiedKFold(n_splits = 5, random_state = seed)
print(OPMAnalysisDataNoFamBinary.shape)
print(cv)
(14920, 100)
StratifiedKFold(n_splits=5, random_state=14920, shuffle=False)

Random Forest Classification

Max Depth The maximum depth (levels) in the tree. When a value is set, the tree may not split further once this level has been met regardless of how many nodes are in the leaf.

Max Features Number of features to consider when looking for a split.

Minimum Samples in Leaf Minimum number of samples required to be in a leaf node. Splits may not occur which cause the number of samples in a leaf to be less than this value. Too low a value here leads to overfitting the tree to train data.

Minimum Samples to Split Minimum number fo samples required to split a node. Care was taken during parameter tests to keep the ratio between Min Samples in Leaf and Min Samples to Split equal to that of the default values (1:2). This was done to allow an even 50/50 split on nodes which match the lowest granularity split criteria. similar to the min samples in leaf, too low a value here leads to overfitting the tree to train data.

n_estimators Number of Trees generated in the forest. Increasing the number of trees, in our models increased accuracy while decreasing performance. We tuned to provide output that completed all 10 iterations in under 10 minutes.

Not Complete#### After 13 iterations of modifying the above parameters, we land on a final winner based on the highest average Accuracy value across all iterations. Average Accuracy values in our 10 test/train iterations ranged from 70.2668 % from default inputs of the random forest classification model to a value of 72.5192 % in the best tuned model fit. Although the run-time of this model parameter choice is the largest performed, we decided to remain with these inputs due to the amount increase in accuracy. As mentioned previously, we tuned the n_estimators parameter to ensure we stayed under 10 minutes execution. Parameter inputs for the final Random Forest Classification model with the KD Tree Algorithm are as follows: ###Not Complete

max_depth max_features min_samples_leaf min_samples_split n_estimators
TBD TBD TBD TBD TBD
In [113]:
%%time

def rfc_explorBinary(n_estimators,
               max_features,
               max_depth, 
               min_samples_split,
               min_samples_leaf,
               Data        = OPMAnalysisDataNoFamBinary,
               cols        = PCList,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data[cols].as_matrix()
    
    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, n_jobs=-1, random_state = seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('CLF',rfc_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs
Wall time: 9.3 µs
In [114]:
%%time

def rfc_explorBinary_w_PCA(n_estimators,
               max_features,
               max_depth, 
               min_samples_split,
               min_samples_leaf,
               PCA,
               Data        = OPMAnalysisDataNoFamBinary,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data.drop("SEP", axis=1).as_matrix()
    
    rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, n_jobs=-1, random_state = seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('PCA', PCA),
         ('CLF',rfc_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs
Wall time: 9.06 µs
In [115]:
%%time
FinalResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])

TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])

acclist = [] 
fullColumns = list(OPMAnalysisDataNoFamBinary.columns)

for i in fullColumns:
    if i == "SEP": fullColumns.remove(i)

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10   , 10   , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 15    , 20    , 30    , 50    ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 5     , 10    , 15   , 20   , None  , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    , 15    ] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , None  , None  , None , None , None  , 10    , 15    , 20    , 25    , 30    , 17    , 18    , 19    , 21    , 22    , 23    , 15    , 15    , 15    , 15    ] 
min_samples_split  =  [2     , 8      , 12    , 18    , 20    , 24    , 36    , 48    , 36    , 36    , 36    , 36  , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    , 36    ] 
min_samples_leaf   =  [1     , 4      , 6     , 9     , 10    , 12    , 18    , 24    , 18    , 18    , 18    , 18  , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    ]

##Model with all Raw Scaled Features
for i in range(0,len(n_estimators)):
    acclist.append(rfc_explorBinary(n_estimators      = n_estimators[i],
                              max_features      = max_features[i],
                              max_depth         = max_depth[i],
                              min_samples_split = min_samples_split[i],
                              min_samples_leaf  = min_samples_leaf[i],
                              cols              = fullColumns
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Random Forest: All Raw Features",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist


acclist = []

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10   , 10   , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 15    , 20    , 30    , 50    ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 5     , 10    , 15   , 20   , None  , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto'] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , None  , None  , None , None , None  , 10    , 15    , 20    , 25    , 30    , 17    , 18    , 19    , 21    , 22    , 23    , 17    , 17    , 17    , 17    ] 
min_samples_split  =  [2     , 8      , 12    , 18    , 20    , 24    , 36    , 48    , 48    , 48    , 48   , 48   , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    , 48    ]  
min_samples_leaf   =  [1     , 4      , 6     , 9     , 10    , 12    , 18    , 24    , 24    , 24    , 24   , 24   , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    ]


## Model with only top 15 raw Scaled Principal Features 
for i in range(0,len(n_estimators)):
    acclist.append(rfc_explorBinary(n_estimators      = n_estimators[i],
                              max_features      = max_features[i],
                              max_depth         = max_depth[i],
                              min_samples_split = min_samples_split[i],
                              min_samples_leaf  = min_samples_leaf[i]
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Random Forest: Top 15 Raw from PC",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist


### Model with PCA
acclist = []

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10   , 10   , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 15    , 20    , 30    , 50    ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 5     , 10    , 15   , 20   , None  , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto'] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , None  , None , None , None  , 10    , 15    , 20    , 25    , 30    , 17    , 18    , 19    , 21    , 22    , 23    , 15    , 15    , 15    , 15    ] 
min_samples_split  =  [2     , 8      , 12    , 18    , 20    , 24    , 36    ,  24   , 24    , 24    , 24    , 24  , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    , 24    ]   
min_samples_leaf   =  [1     , 4      , 6     , 9     , 10    , 12    , 18    ,  12   , 12    , 12    , 12    , 12  , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    , 12    ]       


for i in range(0,len(n_estimators)):
    acclist.append(rfc_explorBinary_w_PCA(n_estimators      = n_estimators[i],
                                    max_features      = max_features[i],
                                    max_depth         = max_depth[i],
                                    min_samples_split = min_samples_split[i],
                                    min_samples_leaf  = min_samples_leaf[i],
                                    PCA               = PCA(n_components=22, svd_solver='randomized', random_state = seed)
                                   )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Random Forest: With PCA",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']

display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
ModelVersion max_depth max_features min_samples_leaf min_samples_split n_estimators Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Random Forest: All Raw Features NaN auto 1 2 10 0.597320 0.697822 0.714477 0.739524 0.694268 0.688682 00:00:01.417801
1 Random Forest: All Raw Features NaN auto 4 8 10 0.628811 0.706868 0.717493 0.755615 0.713041 0.704365 00:00:01.434335
2 Random Forest: All Raw Features NaN auto 6 12 10 0.605360 0.715578 0.717828 0.735836 0.725444 0.700009 00:00:01.406323
3 Random Forest: All Raw Features NaN auto 9 18 10 0.640536 0.702178 0.726877 0.756621 0.721086 0.709459 00:00:01.429482
4 Random Forest: All Raw Features NaN auto 10 20 10 0.581910 0.701508 0.731233 0.766678 0.723768 0.701019 00:00:01.435840
5 Random Forest: All Raw Features NaN auto 12 24 10 0.623451 0.686432 0.720509 0.769695 0.715387 0.703095 00:00:01.437422
6 Random Forest: All Raw Features NaN auto 18 36 10 0.660302 0.712898 0.714142 0.753269 0.722427 0.712607 00:00:01.433532
7 Random Forest: All Raw Features NaN auto 24 48 10 0.619430 0.719598 0.714142 0.740530 0.715387 0.701817 00:00:01.426297
8 Random Forest: All Raw Features NaN 5 18 36 10 0.655946 0.709883 0.711796 0.732819 0.714381 0.704965 00:00:01.428529
9 Random Forest: All Raw Features NaN 10 18 36 10 0.682412 0.695812 0.715147 0.748910 0.732819 0.715020 00:00:01.430954
10 Random Forest: All Raw Features NaN 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.422960
11 Random Forest: All Raw Features NaN 20 18 36 10 0.617085 0.730988 0.706434 0.768689 0.707006 0.706041 00:00:01.427748
12 Random Forest: All Raw Features NaN None 18 36 10 0.611390 0.725293 0.725536 0.767684 0.697955 0.705572 00:00:01.932891
13 Random Forest: All Raw Features 10.0 15 18 36 10 0.636181 0.721608 0.694370 0.755950 0.728461 0.707314 00:00:01.427343
14 Random Forest: All Raw Features 15.0 15 18 36 10 0.651926 0.725628 0.718834 0.757291 0.738183 0.718373 00:00:01.434472
15 Random Forest: All Raw Features 20.0 15 18 36 10 0.625461 0.726633 0.725201 0.767684 0.734160 0.715828 00:00:01.430002
16 Random Forest: All Raw Features 25.0 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.432953
17 Random Forest: All Raw Features 30.0 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.427726
18 Random Forest: All Raw Features 17.0 15 18 36 10 0.617420 0.717588 0.725201 0.763996 0.734831 0.711807 00:00:01.433504
19 Random Forest: All Raw Features 18.0 15 18 36 10 0.651926 0.711223 0.725201 0.764666 0.735166 0.717637 00:00:01.428864
20 Random Forest: All Raw Features 19.0 15 18 36 10 0.638526 0.721273 0.725201 0.766678 0.734160 0.717168 00:00:01.426117
21 Random Forest: All Raw Features 21.0 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.427356
22 Random Forest: All Raw Features 22.0 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.426489
23 Random Forest: All Raw Features 23.0 15 18 36 10 0.625461 0.726633 0.725201 0.768019 0.734160 0.715895 00:00:01.429544
24 Random Forest: All Raw Features 15.0 15 18 36 15 0.649246 0.727973 0.712802 0.758297 0.735166 0.716697 00:00:01.462569
25 Random Forest: All Raw Features 15.0 15 18 36 20 0.629146 0.715243 0.718164 0.760644 0.729132 0.710466 00:00:01.496566
26 Random Forest: All Raw Features 15.0 15 18 36 30 0.623451 0.702848 0.720509 0.762320 0.728126 0.707451 00:00:01.542697
27 Random Forest: All Raw Features 15.0 15 18 36 50 0.643886 0.714908 0.722855 0.762655 0.726450 0.714151 00:00:02.178267
ModelVersion max_depth max_features min_samples_leaf min_samples_split n_estimators Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Random Forest: Top 15 Raw from PC NaN auto 1 2 10 0.556449 0.682412 0.732574 0.743547 0.710694 0.685135 00:00:01.358763
1 Random Forest: Top 15 Raw from PC NaN auto 4 8 10 0.591625 0.694137 0.730563 0.749916 0.730808 0.699410 00:00:01.369868
2 Random Forest: Top 15 Raw from PC NaN auto 6 12 10 0.571859 0.697487 0.698056 0.769024 0.723433 0.691972 00:00:01.372607
3 Random Forest: Top 15 Raw from PC NaN auto 9 18 10 0.595980 0.697822 0.737936 0.767684 0.737848 0.707454 00:00:01.365607
4 Random Forest: Top 15 Raw from PC NaN auto 10 20 10 0.605025 0.714573 0.732239 0.760979 0.723433 0.707250 00:00:01.372262
5 Random Forest: Top 15 Raw from PC NaN auto 12 24 10 0.634841 0.709548 0.728217 0.756621 0.733155 0.712476 00:00:01.364441
6 Random Forest: Top 15 Raw from PC NaN auto 18 36 10 0.649246 0.712898 0.717493 0.759638 0.731143 0.714084 00:00:01.369521
7 Random Forest: Top 15 Raw from PC NaN auto 24 48 10 0.639866 0.721608 0.724196 0.752263 0.743882 0.716363 00:00:01.361159
8 Random Forest: Top 15 Raw from PC NaN 5 24 48 10 0.645226 0.717588 0.714477 0.746229 0.714717 0.707647 00:00:01.365186
9 Random Forest: Top 15 Raw from PC NaN 10 24 48 10 0.607705 0.706868 0.731568 0.759638 0.730808 0.707317 00:00:01.364963
10 Random Forest: Top 15 Raw from PC NaN 15 24 48 10 0.637521 0.708543 0.726542 0.767684 0.716728 0.711403 00:00:01.367613
11 Random Forest: Top 15 Raw from PC NaN 20 24 48 10 0.613065 0.730653 0.716823 0.772377 0.705665 0.707717 00:00:01.366637
12 Random Forest: Top 15 Raw from PC NaN None 24 48 10 0.608710 0.744724 0.715483 0.760979 0.716058 0.709191 00:00:01.473873
13 Random Forest: Top 15 Raw from PC 10.0 auto 24 48 10 0.630486 0.712228 0.722185 0.755950 0.722092 0.708588 00:00:01.365959
14 Random Forest: Top 15 Raw from PC 15.0 auto 24 48 10 0.621441 0.725628 0.714812 0.757962 0.735836 0.711136 00:00:01.375449
15 Random Forest: Top 15 Raw from PC 20.0 auto 24 48 10 0.639866 0.721608 0.724196 0.758967 0.743882 0.717704 00:00:01.364911
16 Random Forest: Top 15 Raw from PC 25.0 auto 24 48 10 0.639866 0.721608 0.724196 0.752263 0.743882 0.716363 00:00:01.365343
17 Random Forest: Top 15 Raw from PC 30.0 auto 24 48 10 0.639866 0.721608 0.724196 0.752263 0.743882 0.716363 00:00:01.371047
18 Random Forest: Top 15 Raw from PC 17.0 auto 24 48 10 0.639866 0.723953 0.724531 0.762320 0.743882 0.718910 00:00:01.366764
19 Random Forest: Top 15 Raw from PC 18.0 auto 24 48 10 0.639866 0.721943 0.725871 0.754609 0.743882 0.717234 00:00:01.367283
20 Random Forest: Top 15 Raw from PC 19.0 auto 24 48 10 0.639866 0.721608 0.725871 0.750251 0.743882 0.716296 00:00:01.365535
21 Random Forest: Top 15 Raw from PC 21.0 auto 24 48 10 0.639866 0.721608 0.724196 0.752263 0.743882 0.716363 00:00:01.364379
22 Random Forest: Top 15 Raw from PC 22.0 auto 24 48 10 0.639866 0.721608 0.724196 0.755615 0.743882 0.717033 00:00:01.369181
23 Random Forest: Top 15 Raw from PC 23.0 auto 24 48 10 0.639866 0.721608 0.724196 0.752263 0.743882 0.716363 00:00:01.364247
24 Random Forest: Top 15 Raw from PC 17.0 auto 24 48 15 0.635846 0.724958 0.722185 0.752598 0.728126 0.712743 00:00:01.538879
25 Random Forest: Top 15 Raw from PC 17.0 auto 24 48 20 0.635846 0.719933 0.720845 0.748910 0.734831 0.712073 00:00:01.433106
26 Random Forest: Top 15 Raw from PC 17.0 auto 24 48 30 0.649916 0.711223 0.725536 0.757627 0.731814 0.715223 00:00:01.495285
27 Random Forest: Top 15 Raw from PC 17.0 auto 24 48 50 0.631826 0.707873 0.719169 0.758297 0.731143 0.709662 00:00:01.621999
ModelVersion max_depth max_features min_samples_leaf min_samples_split n_estimators Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Random Forest: With PCA NaN auto 1 2 10 0.553099 0.554439 0.695710 0.688904 0.656051 0.629641 00:00:02.179145
1 Random Forest: With PCA NaN auto 4 8 10 0.630151 0.635176 0.706434 0.706336 0.699631 0.675546 00:00:01.970614
2 Random Forest: With PCA NaN auto 6 12 10 0.629146 0.616750 0.693029 0.700972 0.699631 0.667906 00:00:02.169322
3 Random Forest: With PCA NaN auto 9 18 10 0.659631 0.683417 0.706434 0.713711 0.720416 0.696722 00:00:02.025520
4 Random Forest: With PCA NaN auto 10 20 10 0.639866 0.654941 0.681971 0.716728 0.714717 0.681645 00:00:01.953846
5 Random Forest: With PCA NaN auto 12 24 10 0.653601 0.663987 0.700067 0.725109 0.716728 0.691898 00:00:01.996375
6 Random Forest: With PCA NaN auto 18 36 10 0.651591 0.647906 0.705429 0.712370 0.705665 0.684592 00:00:02.041022
7 Random Forest: With PCA NaN 5 12 24 10 0.655946 0.672697 0.697721 0.687563 0.724774 0.687740 00:00:02.158300
8 Random Forest: With PCA NaN 10 12 24 10 0.640201 0.618425 0.704759 0.703989 0.702313 0.673938 00:00:02.436428
9 Random Forest: With PCA NaN 15 12 24 10 0.637186 0.583250 0.695040 0.710023 0.720416 0.669183 00:00:02.705808
10 Random Forest: With PCA NaN 20 12 24 10 0.665997 0.568174 0.708445 0.707006 0.713041 0.672533 00:00:02.911051
11 Random Forest: With PCA NaN None 12 24 10 0.672362 0.573534 0.703418 0.695944 0.710023 0.671056 00:00:02.932878
12 Random Forest: With PCA 10.0 auto 12 24 10 0.647236 0.678392 0.708445 0.732484 0.718404 0.696992 00:00:01.974182
13 Random Forest: With PCA 15.0 auto 12 24 10 0.663987 0.694807 0.690349 0.724103 0.718740 0.698397 00:00:02.066852
14 Random Forest: With PCA 20.0 auto 12 24 10 0.647236 0.643216 0.687332 0.724774 0.716393 0.683790 00:00:02.123031
15 Random Forest: With PCA 25.0 auto 12 24 10 0.653601 0.663987 0.700402 0.725109 0.717399 0.692100 00:00:01.991082
16 Random Forest: With PCA 30.0 auto 12 24 10 0.653601 0.663987 0.700067 0.725109 0.716728 0.691898 00:00:01.985148
17 Random Forest: With PCA 17.0 auto 12 24 10 0.648576 0.607035 0.696046 0.717399 0.717399 0.677291 00:00:02.068668
18 Random Forest: With PCA 18.0 auto 12 24 10 0.648241 0.655276 0.684651 0.731478 0.720080 0.687946 00:00:02.161763
19 Random Forest: With PCA 19.0 auto 12 24 10 0.651256 0.646901 0.690013 0.724103 0.716728 0.685800 00:00:02.024107
20 Random Forest: With PCA 21.0 auto 12 24 10 0.648911 0.658291 0.687332 0.721421 0.718740 0.686939 00:00:02.114561
21 Random Forest: With PCA 22.0 auto 12 24 10 0.649916 0.692462 0.693365 0.727120 0.718740 0.696321 00:00:02.057114
22 Random Forest: With PCA 23.0 auto 12 24 10 0.653601 0.663317 0.696716 0.728461 0.718404 0.692100 00:00:02.060058
23 Random Forest: With PCA 15.0 auto 12 24 15 0.640871 0.700503 0.698391 0.720416 0.725779 0.697192 00:00:02.092665
24 Random Forest: With PCA 15.0 auto 12 24 20 0.639196 0.680402 0.702078 0.726785 0.714717 0.692636 00:00:02.050527
25 Random Forest: With PCA 15.0 auto 12 24 30 0.650921 0.700168 0.701408 0.723768 0.719075 0.699068 00:00:02.474255
26 Random Forest: With PCA 15.0 auto 12 24 50 0.649916 0.708543 0.700402 0.721086 0.725779 0.701145 00:00:02.638342
CPU times: user 6min 2s, sys: 10min 23s, total: 16min 26s
Wall time: 2min 20s
In [116]:
display(TopResultsDF)

plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))

FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
ModelVersion Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy
0 Random Forest: Top 15 Raw from PC 0.639866 0.723953 0.724531 0.762320 0.743882 0.718910
1 Random Forest: All Raw Features 0.651926 0.725628 0.718834 0.757291 0.738183 0.718373
2 Random Forest: With PCA 0.649916 0.708543 0.700402 0.721086 0.725779 0.701145
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

We have created a function to be re-used for our cross-validation Accuracy Scores. Inputs of PCA components, Model CLF object, original sample data, and a CV containing our test/train splits allow us to easily produce an array of Accuracy Scores for the different permutations of models tested. A XXXXXXTBDXXXXX plot is also displayed depicting a view of the misclassification values for each iteration. Finally, a confusion matrix is displayed for the last test/train iteration for further interpretation on results.

In [117]:
def plot_confusion_matrixBinary(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.rcParams['figure.figsize'] = (18, 6)
    plt.rcParams.update({'font.size': 16})
    plt.rc('xtick', labelsize=18)
    plt.rc('ytick', labelsize=18) 
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize = 18)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, round(cm[i, j],2),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label', fontsize = 18)
    plt.xlabel('Predicted label', fontsize = 18)

    plt.show()
In [118]:
def plot_ROC_curve(X, y, mean_tpr, mean_fpr, cv = cv, ):
    
    plt.rcParams['figure.figsize'] = (12, 6)

    lw = 2

    plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
             label='Luck')

    mean_tpr /= cv.get_n_splits(X, y)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
             label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)

    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic (ROC) Curve')
    plt.legend(loc="lower right")
    plt.show()
In [119]:
%%time

def compute_kfold_scores_ClassificationBinary( clf,
                                         PCA = "No",
                                         Data     = OPMAnalysisDataNoFamBinary,
                                         cols     = PCList,
                                         cv       = cv):

    y = Data["SEP"].values # get the labels we want    
    
    y = np.where(y == 'NS', 0, 1) # NS = 0; SC = 1
    
    X = Data[cols].as_matrix()


    # Run classifier with cross-validation and plot ROC curves

    # setup pipeline to take PCA, then fit a clf model
    if(PCA == "No"):
        clf_pipe = Pipeline(
            [('minMaxScaler', MinMaxScaler()),
             ('CLF',clf)]
        )
    else:
        clf_pipe = Pipeline(
            [('minMaxScaler', MinMaxScaler()),
             ('PCA', PCA),
             ('CLF',clf)]
        )
    
    colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])
    
    mean_tpr = 0.0
    mean_fpr = np.linspace(0, 1, 100)
    lw = 2
    i = 0
    
    accuracy = []
    #logloss = []
    
    for (train, test), color in zip(cv.split(X, y), colors):
        clf_pipe.fit(X[train],y[train])  # train object
        y_hat = clf_pipe.predict(X[test]) # get test set preditions
        
        probas_ = clf_pipe.fit(X[train], y[train]).predict_proba(X[test])
        
        # Compute ROC curve and area the curve
        fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
        mean_tpr += interp(mean_fpr, fpr, tpr)
        mean_tpr[0] = 0.0
        roc_auc = auc(fpr, tpr)
        
        plt.rcParams['figure.figsize'] = (12, 6)
        
        plt.plot(fpr, tpr, lw=lw, color=color,
                 label='ROC fold %d (area = %0.2f)' % (i, roc_auc))

        i += 1
    
    plot_ROC_curve(X, y, mean_tpr, mean_fpr)         
        #logloss.append(round(l,5)) 
    
    #print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))

    for (train, test), color in zip(cv.split(X, y), colors):
        clf_pipe.fit(X[train],y[train])  # train object
        y_hat = clf_pipe.predict(X[test]) # get test set preditions
        
        a = float(mt.accuracy_score(y[test],y_hat))
        #l = float(mt.log_loss(y[test], y_hat))
        
        accuracy.append(round(a,5)) 
        
        ytestnames = np.where(y[test] ==  0,'NS','SC')

        yhatnames  = np.where(y_hat ==  0,'NS', 'SC')

        #print(set(list(y_hat)))
        print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))

            # Plot non-normalized confusion matrix
        plt.figure()
        plot_confusion_matrixBinary(confusion_matrix(y[test], y_hat), 
                              classes   =["NS",  "SC"], 
                              normalize =True,
                              title     ='Confusion matrix, with normalization')
    
    print("Accuracy Ratings across all iterations: {0}\n\n\
        Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))

    return clf_pipe.named_steps['CLF'], accuracy
CPU times: user 6 µs, sys: 0 ns, total: 6 µs
Wall time: 10.3 µs
In [120]:
%%time

rfc_clf = RandomForestClassifier(n_estimators       = 10, 
                                 max_features       = 'auto', 
                                 max_depth          = 17.0, 
                                 min_samples_split  = 48, 
                                 min_samples_leaf   = 24,
                                 n_jobs             = -1, 
                                 random_state       = seed) # get object
    
rfc_clf, rfc_acc = compute_kfold_scores_ClassificationBinary(rfc_clf, 
                                                             ##PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed),
                                                             cols = PCList)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          796   704  1500
SC          371  1114  1485
All        1167  1818  2985

Normalized confusion matrix
[[ 0.53066667  0.46933333]
 [ 0.24983165  0.75016835]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS   SC   All
True                      
NS         1350  150  1500
SC          674  811  1485
All        2024  961  2985

Normalized confusion matrix
[[ 0.9         0.1       ]
 [ 0.45387205  0.54612795]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted   NS    SC   All
True                      
NS         773   726  1499
SC          96  1389  1485
All        869  2115  2984

Normalized confusion matrix
[[ 0.51567712  0.48432288]
 [ 0.06464646  0.93535354]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          944   555  1499
SC          154  1330  1484
All        1098  1885  2983

Normalized confusion matrix
[[ 0.62975317  0.37024683]
 [ 0.10377358  0.89622642]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          984   515  1499
SC          249  1235  1484
All        1233  1750  2983

Normalized confusion matrix
[[ 0.65643763  0.34356237]
 [ 0.16778976  0.83221024]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.63987, 0.72395, 0.72453, 0.76232, 0.74388]

        Average Accuracy: 0.71891

CPU times: user 6.49 s, sys: 2.39 s, total: 8.89 s
Wall time: 5.82 s

Build a Binary KNN Model

KNN

Algorithm

Options include "Ball Tree" and "KD Tree".

  • Ball Trees are binary trees formed from nodes of multidimensional hyperspheres, or "balls". Node hyperspheres may intersect, but each point is assigned to one according to distance from the hypersphere center.
  • KD Trees are binary trees formed from nodes of multidimensional hyperplanes. Every node in the tree is associated with one of the dimensions, with the hyperplane perpendicular to that dimension's axis.

Our findings, were that the Ball Tree algorithm was considerably less efficient to produce results for all 10 iterations in comparison to the KD Tree Algorithm.

Leaf Size

The size for leaf nodes in the KNN Tree.

Number of Neighbors

After 24 iterations of modifying the above parameters, we land on a final winner based on the highest average Accuracy value across all iterations. Average Accuracy values in our 10 test/train iterations ranged from 66.5216 % from the worst parameter inputs of the Ball_Tree Algorith to a value of 69.5528 % in best tuned KNN Classification model fit. We have chosen to utilize the best input for KD tree, although losing an improvement of .0004 % due to the cost(slower runtime of 07 Minutes 25 Seconds through 10 iterations) of fitting the model as Ball Tree. Parameter inputs for the final K Nearest Neighbor Classification model with the KD Tree Algorithm are as follows:

algorithm leaf_size n_neighbors
kd_tree 50 150
In [121]:
%%time


def knn_explorBinary_w_PCA(n_neighbors,
               algorithm ,
               leaf_size,
               PCA,
               Data        = OPMAnalysisDataNoFamBinary,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data.drop("SEP", axis=1).as_matrix() 
    
    knn_clf = KNeighborsClassifier(n_neighbors = n_neighbors, algorithm = algorithm, leaf_size = leaf_size, n_jobs=-1) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('PCA', PCA),
         ('CLF',knn_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
CPU times: user 0 ns, sys: 20 µs, total: 20 µs
Wall time: 12.2 µs
In [122]:
%%time

def knn_explorBinary(n_neighbors,
               algorithm ,
               leaf_size,
               Data        = OPMAnalysisDataNoFamBinary,
               cols        = PCList,
               cv          = cv,
               seed        = seed):
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    if ("SEP" in cols):    X = Data[cols].drop("SEP", axis=1).as_matrix() 
    else: X = Data[cols]
    
    knn_clf = KNeighborsClassifier(n_neighbors = n_neighbors, algorithm = algorithm, leaf_size = leaf_size, n_jobs=-1) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler', MinMaxScaler()),
         ('CLF',knn_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    
    #print(TotalTime)
    #print(accuracy)
    
    return accuracy
CPU times: user 12 µs, sys: 7 µs, total: 19 µs
Wall time: 12.2 µs
In [123]:
%%time
###Full Columns
acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 150        , 150        , 150        , 150        , 150        , 150        ]
algorithm   =  'kd_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]



for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              cols = fullColumns
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", Full Raw Columns",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
display(rfcdf)
del rfcdf, acclist



acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 150        , 150        , 150        , 150        , 150        , 150        ]
algorithm   =  'ball_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]

for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              cols = fullColumns
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", Full Raw Columns",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist

###Reduced Columns

acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 300        , 350        , 400        ,  50        ,  50        ,  50        ,  50        ,  50        ,  50        ]
algorithm   =  'kd_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]



for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i]
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", Reduced Raw Columns",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist



acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 300        , 350        , 400        ,  50        ,  50        ,  50        ,  50        ,  50        ,  50        ]
algorithm   =  'ball_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]

for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i]
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", Reduced Raw Columns",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist


#### WITH PCA

acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 100        , 100        , 100        , 100        , 100        , 100        ]
algorithm   =  'kd_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]



for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary_w_PCA(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", With PCA",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist



acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 100        , 100        , 100        , 100        , 100        , 100        ]
algorithm   =  'ball_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5          , 10         , 20         ]

for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary_w_PCA(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", With PCA",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: kd_tree, Full Raw Columns kd_tree 30 5 0.643216 0.754439 0.658512 0.704325 0.681864 0.688471 00:00:08.892554
1 KNN: kd_tree, Full Raw Columns kd_tree 30 10 0.661977 0.749079 0.673257 0.715052 0.694938 0.698861 00:00:10.841129
2 KNN: kd_tree, Full Raw Columns kd_tree 30 15 0.672027 0.771859 0.698391 0.729132 0.712035 0.716689 00:00:10.568907
3 KNN: kd_tree, Full Raw Columns kd_tree 30 20 0.664992 0.766834 0.693365 0.731143 0.707677 0.712802 00:00:12.004494
4 KNN: kd_tree, Full Raw Columns kd_tree 30 30 0.658961 0.764489 0.697721 0.738183 0.710694 0.714010 00:00:13.166757
5 KNN: kd_tree, Full Raw Columns kd_tree 30 40 0.672027 0.762144 0.698056 0.739189 0.717734 0.717830 00:00:13.255442
6 KNN: kd_tree, Full Raw Columns kd_tree 30 50 0.684087 0.768509 0.693365 0.734495 0.718404 0.719772 00:00:14.191612
7 KNN: kd_tree, Full Raw Columns kd_tree 30 100 0.699162 0.761474 0.699397 0.743212 0.725444 0.725738 00:00:15.058125
8 KNN: kd_tree, Full Raw Columns kd_tree 30 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:15.777057
9 KNN: kd_tree, Full Raw Columns kd_tree 30 200 0.693467 0.757789 0.711126 0.737513 0.726450 0.725269 00:00:16.462795
10 KNN: kd_tree, Full Raw Columns kd_tree 30 250 0.693802 0.750754 0.710121 0.733825 0.717734 0.721247 00:00:16.685488
11 KNN: kd_tree, Full Raw Columns kd_tree 2 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:30.047373
12 KNN: kd_tree, Full Raw Columns kd_tree 3 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:24.239186
13 KNN: kd_tree, Full Raw Columns kd_tree 4 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:26.522597
14 KNN: kd_tree, Full Raw Columns kd_tree 5 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:23.034559
15 KNN: kd_tree, Full Raw Columns kd_tree 10 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:18.151222
16 KNN: kd_tree, Full Raw Columns kd_tree 20 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:15.686835
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: ball_tree, Full Raw Columns ball_tree 30 5 0.643551 0.754439 0.658512 0.704325 0.681864 0.688538 00:00:12.058221
1 KNN: ball_tree, Full Raw Columns ball_tree 30 10 0.661977 0.749079 0.673257 0.715052 0.694938 0.698861 00:00:12.042084
2 KNN: ball_tree, Full Raw Columns ball_tree 30 15 0.672027 0.771859 0.698391 0.729132 0.712035 0.716689 00:00:11.752670
3 KNN: ball_tree, Full Raw Columns ball_tree 30 20 0.664992 0.766834 0.693365 0.731143 0.707677 0.712802 00:00:11.563774
4 KNN: ball_tree, Full Raw Columns ball_tree 30 30 0.658961 0.764489 0.697721 0.738183 0.710694 0.714010 00:00:11.471676
5 KNN: ball_tree, Full Raw Columns ball_tree 30 40 0.672027 0.762144 0.698056 0.739189 0.717734 0.717830 00:00:11.609961
6 KNN: ball_tree, Full Raw Columns ball_tree 30 50 0.684087 0.768509 0.693365 0.734495 0.718404 0.719772 00:00:09.546941
7 KNN: ball_tree, Full Raw Columns ball_tree 30 100 0.699162 0.761474 0.699397 0.743212 0.725444 0.725738 00:00:12.384616
8 KNN: ball_tree, Full Raw Columns ball_tree 30 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:12.284001
9 KNN: ball_tree, Full Raw Columns ball_tree 30 200 0.693467 0.757789 0.711126 0.737513 0.726450 0.725269 00:00:11.967304
10 KNN: ball_tree, Full Raw Columns ball_tree 30 250 0.693802 0.750754 0.710121 0.733825 0.717734 0.721247 00:00:12.983733
11 KNN: ball_tree, Full Raw Columns ball_tree 2 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:23.608123
12 KNN: ball_tree, Full Raw Columns ball_tree 3 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:17.146252
13 KNN: ball_tree, Full Raw Columns ball_tree 4 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:18.443665
14 KNN: ball_tree, Full Raw Columns ball_tree 5 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:17.313255
15 KNN: ball_tree, Full Raw Columns ball_tree 10 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:14.465738
16 KNN: ball_tree, Full Raw Columns ball_tree 20 150 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408 00:00:12.693938
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: kd_tree, Reduced Raw Columns kd_tree 30 5 0.608040 0.757789 0.656166 0.710023 0.686222 0.683648 00:00:05.077079
1 KNN: kd_tree, Reduced Raw Columns kd_tree 30 10 0.628141 0.735343 0.660188 0.723098 0.700302 0.689414 00:00:05.738448
2 KNN: kd_tree, Reduced Raw Columns kd_tree 30 15 0.661977 0.773869 0.669906 0.738518 0.714717 0.711797 00:00:05.560124
3 KNN: kd_tree, Reduced Raw Columns kd_tree 30 20 0.661307 0.769179 0.682306 0.733490 0.709353 0.711127 00:00:06.106267
4 KNN: kd_tree, Reduced Raw Columns kd_tree 30 30 0.684087 0.762814 0.690684 0.735166 0.713711 0.717292 00:00:06.732442
5 KNN: kd_tree, Reduced Raw Columns kd_tree 30 40 0.684757 0.764824 0.693365 0.733490 0.722762 0.719840 00:00:07.428523
6 KNN: kd_tree, Reduced Raw Columns kd_tree 30 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:08.133567
7 KNN: kd_tree, Reduced Raw Columns kd_tree 30 100 0.690117 0.761474 0.701408 0.732819 0.718404 0.720844 00:00:09.697076
8 KNN: kd_tree, Reduced Raw Columns kd_tree 30 150 0.694807 0.756449 0.701408 0.730473 0.714381 0.719504 00:00:09.578428
9 KNN: kd_tree, Reduced Raw Columns kd_tree 30 200 0.703853 0.746064 0.707105 0.726785 0.717734 0.720308 00:00:10.799729
10 KNN: kd_tree, Reduced Raw Columns kd_tree 30 250 0.702848 0.744054 0.706769 0.729802 0.714381 0.719571 00:00:12.598266
11 KNN: kd_tree, Reduced Raw Columns kd_tree 30 300 0.705528 0.743719 0.707775 0.728797 0.712370 0.719638 00:00:11.663727
12 KNN: kd_tree, Reduced Raw Columns kd_tree 30 350 0.704858 0.741039 0.711126 0.726785 0.711364 0.719034 00:00:13.179267
13 KNN: kd_tree, Reduced Raw Columns kd_tree 30 400 0.705528 0.738693 0.710121 0.725779 0.711700 0.718364 00:00:12.323243
14 KNN: kd_tree, Reduced Raw Columns kd_tree 2 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:08.761683
15 KNN: kd_tree, Reduced Raw Columns kd_tree 3 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:07.532709
16 KNN: kd_tree, Reduced Raw Columns kd_tree 4 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:06.598590
17 KNN: kd_tree, Reduced Raw Columns kd_tree 5 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:08.184010
18 KNN: kd_tree, Reduced Raw Columns kd_tree 10 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:07.160272
19 KNN: kd_tree, Reduced Raw Columns kd_tree 20 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:07.124925
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: ball_tree, Reduced Raw Columns ball_tree 30 5 0.608040 0.757789 0.656166 0.710023 0.686557 0.683715 00:00:11.299193
1 KNN: ball_tree, Reduced Raw Columns ball_tree 30 10 0.628141 0.735343 0.660188 0.723098 0.700302 0.689414 00:00:11.067071
2 KNN: ball_tree, Reduced Raw Columns ball_tree 30 15 0.661977 0.773869 0.669906 0.738518 0.714717 0.711797 00:00:12.508619
3 KNN: ball_tree, Reduced Raw Columns ball_tree 30 20 0.661307 0.769179 0.682306 0.733490 0.709353 0.711127 00:00:11.206229
4 KNN: ball_tree, Reduced Raw Columns ball_tree 30 30 0.684087 0.762814 0.690684 0.735166 0.713711 0.717292 00:00:11.215746
5 KNN: ball_tree, Reduced Raw Columns ball_tree 30 40 0.684757 0.764824 0.693365 0.733490 0.722762 0.719840 00:00:12.355972
6 KNN: ball_tree, Reduced Raw Columns ball_tree 30 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:11.024150
7 KNN: ball_tree, Reduced Raw Columns ball_tree 30 100 0.690117 0.761474 0.701408 0.732819 0.718404 0.720844 00:00:11.177232
8 KNN: ball_tree, Reduced Raw Columns ball_tree 30 150 0.694807 0.756449 0.701408 0.730473 0.714381 0.719504 00:00:12.159429
9 KNN: ball_tree, Reduced Raw Columns ball_tree 30 200 0.703853 0.746064 0.707105 0.726785 0.717734 0.720308 00:00:11.769993
10 KNN: ball_tree, Reduced Raw Columns ball_tree 30 250 0.702848 0.744054 0.706769 0.729802 0.714381 0.719571 00:00:11.892291
11 KNN: ball_tree, Reduced Raw Columns ball_tree 30 300 0.705528 0.743719 0.707775 0.728797 0.712370 0.719638 00:00:12.674450
12 KNN: ball_tree, Reduced Raw Columns ball_tree 30 350 0.704858 0.741039 0.711126 0.726785 0.711364 0.719034 00:00:12.867783
13 KNN: ball_tree, Reduced Raw Columns ball_tree 30 400 0.705528 0.738693 0.710121 0.725779 0.711700 0.718364 00:00:11.900334
14 KNN: ball_tree, Reduced Raw Columns ball_tree 2 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:19.401572
15 KNN: ball_tree, Reduced Raw Columns ball_tree 3 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:17.179171
16 KNN: ball_tree, Reduced Raw Columns ball_tree 4 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:16.449001
17 KNN: ball_tree, Reduced Raw Columns ball_tree 5 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:15.570989
18 KNN: ball_tree, Reduced Raw Columns ball_tree 10 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:15.276650
19 KNN: ball_tree, Reduced Raw Columns ball_tree 20 50 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385 00:00:12.069800
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: kd_tree, With PCA kd_tree 30 5 0.592295 0.685427 0.656836 0.683205 0.690245 0.661602 00:00:04.352528
1 KNN: kd_tree, With PCA kd_tree 30 10 0.611725 0.715243 0.657842 0.709353 0.701643 0.679161 00:00:04.900397
2 KNN: kd_tree, With PCA kd_tree 30 15 0.652261 0.758794 0.664209 0.716393 0.719075 0.702146 00:00:05.105806
3 KNN: kd_tree, With PCA kd_tree 30 20 0.664657 0.756784 0.673592 0.728797 0.719075 0.708581 00:00:05.732926
4 KNN: kd_tree, With PCA kd_tree 30 30 0.676047 0.759129 0.687332 0.733490 0.720751 0.715350 00:00:06.457896
5 KNN: kd_tree, With PCA kd_tree 30 40 0.690452 0.766164 0.688003 0.732149 0.722092 0.719772 00:00:06.832854
6 KNN: kd_tree, With PCA kd_tree 30 50 0.690452 0.765494 0.691354 0.733155 0.716728 0.719437 00:00:07.767944
7 KNN: kd_tree, With PCA kd_tree 30 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:09.162034
8 KNN: kd_tree, With PCA kd_tree 30 150 0.688107 0.754104 0.709786 0.726785 0.715722 0.718901 00:00:09.700770
9 KNN: kd_tree, With PCA kd_tree 30 200 0.698157 0.741374 0.713807 0.725779 0.713711 0.718566 00:00:09.566262
10 KNN: kd_tree, With PCA kd_tree 30 250 0.704523 0.742044 0.713472 0.724103 0.720080 0.720844 00:00:10.756348
11 KNN: kd_tree, With PCA kd_tree 2 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:06.720603
12 KNN: kd_tree, With PCA kd_tree 3 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:07.658536
13 KNN: kd_tree, With PCA kd_tree 4 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:07.788528
14 KNN: kd_tree, With PCA kd_tree 5 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:07.817385
15 KNN: kd_tree, With PCA kd_tree 10 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:07.766166
16 KNN: kd_tree, With PCA kd_tree 20 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:08.443829
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: ball_tree, With PCA ball_tree 30 5 0.592295 0.685427 0.656836 0.683205 0.690245 0.661602 00:00:09.125466
1 KNN: ball_tree, With PCA ball_tree 30 10 0.611725 0.715243 0.657842 0.709353 0.701643 0.679161 00:00:08.240648
2 KNN: ball_tree, With PCA ball_tree 30 15 0.652261 0.758794 0.664209 0.716393 0.718740 0.702079 00:00:09.088420
3 KNN: ball_tree, With PCA ball_tree 30 20 0.664657 0.756784 0.673592 0.728797 0.719075 0.708581 00:00:09.844682
4 KNN: ball_tree, With PCA ball_tree 30 30 0.676047 0.759129 0.687332 0.733490 0.720751 0.715350 00:00:08.656438
5 KNN: ball_tree, With PCA ball_tree 30 40 0.690452 0.766164 0.688003 0.732149 0.722092 0.719772 00:00:09.541895
6 KNN: ball_tree, With PCA ball_tree 30 50 0.690452 0.765494 0.691354 0.733155 0.716728 0.719437 00:00:10.316945
7 KNN: ball_tree, With PCA ball_tree 30 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:09.381812
8 KNN: ball_tree, With PCA ball_tree 30 150 0.688107 0.754104 0.709786 0.726785 0.715722 0.718901 00:00:10.100195
9 KNN: ball_tree, With PCA ball_tree 30 200 0.698157 0.741374 0.713807 0.725779 0.713711 0.718566 00:00:10.411864
10 KNN: ball_tree, With PCA ball_tree 30 250 0.704523 0.742044 0.713472 0.724103 0.720080 0.720844 00:00:10.095322
11 KNN: ball_tree, With PCA ball_tree 2 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:11.332675
12 KNN: ball_tree, With PCA ball_tree 3 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:11.641311
13 KNN: ball_tree, With PCA ball_tree 4 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:11.051457
14 KNN: ball_tree, With PCA ball_tree 5 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:10.519241
15 KNN: ball_tree, With PCA ball_tree 10 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:11.181290
16 KNN: ball_tree, With PCA ball_tree 20 100 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526 00:00:09.501107
CPU times: user 13h 37min 34s, sys: 12min 51s, total: 13h 50min 26s
Wall time: 20min 45s
In [124]:
display(TopResultsDF)

plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))

FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
ModelVersion Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy
0 KNN: kd_tree, Full Raw Columns 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408
1 KNN: ball_tree, Full Raw Columns 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408
2 KNN: kd_tree, With PCA 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526
3 KNN: ball_tree, With PCA 0.684087 0.763819 0.708445 0.740194 0.721086 0.723526
4 KNN: kd_tree, Reduced Raw Columns 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385
5 KNN: ball_tree, Reduced Raw Columns 0.692462 0.767504 0.702413 0.732149 0.717399 0.722385
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [125]:
%%time

knn_clf = KNeighborsClassifier(n_neighbors = 150, algorithm = 'ball_tree',leaf_size = 30, n_jobs=-1) # get object

knn_clf, knn_acc = compute_kfold_scores_ClassificationBinary(clf         = knn_clf,
                                                             cols        = fullColumns)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1022   478  1500
SC          431  1054  1485
All        1453  1532  2985

Normalized confusion matrix
[[ 0.68133333  0.31866667]
 [ 0.29023569  0.70976431]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1230   270  1500
SC          438  1047  1485
All        1668  1317  2985

Normalized confusion matrix
[[ 0.82        0.18      ]
 [ 0.29494949  0.70505051]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          835   664  1499
SC          213  1272  1485
All        1048  1936  2984

Normalized confusion matrix
[[ 0.55703803  0.44296197]
 [ 0.14343434  0.85656566]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1055   444  1499
SC          326  1158  1484
All        1381  1602  2983

Normalized confusion matrix
[[ 0.70380254  0.29619746]
 [ 0.21967655  0.78032345]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1072   427  1499
SC          391  1093  1484
All        1463  1520  2983

Normalized confusion matrix
[[ 0.71514343  0.28485657]
 [ 0.26347709  0.73652291]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.69548, 0.76281, 0.7061, 0.74187, 0.72578]

        Average Accuracy: 0.72641

CPU times: user 22min 22s, sys: 3.17 s, total: 22min 25s
Wall time: 34.7 s
In [ ]:
 
In [ ]:
 

Logistic Regression

We have chosen to manipulate the cost variable (C) within our logistic regression analyzing accuracies at {1.0, .01, .05, 5}. This parameter is essentially an inverted regularization strength equal to 1/lambda per scikit-learn class function code (lambda being the actual regularization item). Therefore, the smaller the cost value the stronger the regularization.

In [126]:
mapping = {'NS':0, 'SC':1}
y = OPMAnalysisDataNoFamBinary.replace({'SEP': mapping})
y = y.SEP
In [127]:
%%R -i OPMAnalysisDataNoFamBinary,fullColumns,y
install.packages("car") ## Selection 55
require(car)
str(OPMAnalysisDataNoFamBinary)
print(unlist(fullColumns))
print(paste("# of SEP observations = ", length(y)))
print(paste("SEP type = ", unique(y)))
print(paste("SEP class = ", class(y)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_B". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_C". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_D". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_E". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_F". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_G". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_H". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_I". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_J". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_K". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_01". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_02". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_04". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_05". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_06". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_08". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_09". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_10". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_11". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_12". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_13". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_15". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_16". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_17". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_18". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_19". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_20". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_21". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_22". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_23". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_24". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_25". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_26". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_27". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_28". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_29". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_30". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_31". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_32". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_33". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_34". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_35". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_36". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_37". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_38". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_39". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_40". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_41". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_42". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_44". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_45". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_46". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_47". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_48". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_49". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_50". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_51". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_53". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_54". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_55". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_56". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_10". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_15". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_20". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_30". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_32". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_35". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_38". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_40". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_42". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_44". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_45". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_48". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOCTYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPTYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPGROUP_11". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPGROUP_12". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOATYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOATYP_2". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers.
  (name, str(e)))
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Installing package into ‘/scratch/csegrad/cboomhower/R/x86_64-redhat-linux-gnu-library/3.4’
(as ‘lib’ is unspecified)

  warnings.warn(x, RRuntimeWarning)
Selection: 52
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: trying URL 'https://rweb.crmda.ku.edu/cran/src/contrib/car_2.1-5.tar.gz'

  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Content type 'application/x-gzip'
  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning:  length 628590 bytes (613 KB)

  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: =
  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: 

  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: downloaded 613 KB


  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: 
  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: The downloaded source packages are in
	‘/tmp/RtmpIS3cVP/downloaded_packages’
  warnings.warn(x, RRuntimeWarning)
/users3/csegrad/cboomhower/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Loading required package: car

  warnings.warn(x, RRuntimeWarning)
--- Please select a CRAN mirror for use in this session ---
Secure CRAN mirrors 

 1: 0-Cloud [https]                   2: Algeria [https]                
 3: Australia (Canberra) [https]      4: Australia (Melbourne 1) [https]
 5: Australia (Melbourne 2) [https]   6: Australia (Perth) [https]      
 7: Austria [https]                   8: Belgium (Ghent) [https]        
 9: Brazil (PR) [https]              10: Brazil (RJ) [https]            
11: Brazil (SP 1) [https]            12: Brazil (SP 2) [https]          
13: Bulgaria [https]                 14: Chile 1 [https]                
15: Chile 2 [https]                  16: China (Guangzhou) [https]      
17: China (Lanzhou) [https]          18: Colombia (Cali) [https]        
19: Czech Republic [https]           20: Denmark [https]                
21: Ecuador (Cuenca) [https]         22: Estonia [https]                
23: France (Lyon 1) [https]          24: France (Lyon 2) [https]        
25: France (Marseille) [https]       26: France (Montpellier) [https]   
27: France (Paris 2) [https]         28: Germany (Göttingen) [https]    
29: Germany (Münster) [https]        30: Greece [https]                 
31: Iceland [https]                  32: Indonesia (Jakarta) [https]    
33: Ireland [https]                  34: Italy (Padua) [https]          
35: Japan (Tokyo) [https]            36: Malaysia [https]               
37: Mexico (Mexico City) [https]     38: Norway [https]                 
39: Philippines [https]              40: Serbia [https]                 
41: Spain (A Coruña) [https]         42: Spain (Madrid) [https]         
43: Sweden [https]                   44: Switzerland [https]            
45: Turkey (Denizli) [https]         46: Turkey (Mersin) [https]        
47: UK (Bristol) [https]             48: UK (Cambridge) [https]         
49: UK (London 1) [https]            50: USA (CA 1) [https]             
51: USA (IA) [https]                 52: USA (KS) [https]               
53: USA (MI 1) [https]               54: USA (OR) [https]               
55: USA (TN) [https]                 56: USA (TX 1) [https]             
57: Vietnam [https]                  58: (other mirrors)                


'data.frame':	14920 obs. of  100 variables:
 $ SEP                          : chr  "NS" "NS" "NS" "NS" ...
 $ GSEGRD                       : num  11 12 11 12 13 12 13 13 11 11 ...
 $ IndAvgSalary                 : num  65898 81219 65898 82168 121939 ...
 $ SalaryOverUnderIndAvg        : num  -4041 -9406 -2807 -6547 -26020 ...
 $ LowerLimitAge                : num  20 25 25 25 25 25 25 25 25 25 ...
 $ YearsToRetirement            : num  37 32 32 32 32 32 32 32 32 32 ...
 $ BLS_FEDERAL_OtherSep_Rate    : num  0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ...
 $ BLS_FEDERAL_Quits_Rate       : num  0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ...
 $ BLS_FEDERAL_TotalSep_Level   : int  34 34 34 34 34 34 34 34 34 34 ...
 $ BLS_FEDERAL_JobOpenings_Rate : num  2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 ...
 $ BLS_FEDERAL_OtherSep_Level   : int  10 10 10 10 10 10 10 10 10 10 ...
 $ BLS_FEDERAL_Quits_Level      : int  11 11 11 11 11 11 11 11 11 11 ...
 $ BLS_FEDERAL_JobOpenings_Level: int  58 58 58 58 58 58 58 58 58 58 ...
 $ BLS_FEDERAL_Layoffs_Rate     : num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
 $ BLS_FEDERAL_Layoffs_Level    : int  13 13 13 13 13 13 13 13 13 13 ...
 $ BLS_FEDERAL_TotalSep_Rate    : num  1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 ...
 $ SALARYLog                    : num  11 11.2 11.1 11.2 11.5 ...
 $ LOSSqrt                      : num  2.17 2.68 2 2.41 1.41 ...
 $ SEPCount_EFDATE_OCCLog       : num  5.82 5.82 5.82 4.32 4.14 ...
 $ SEPCount_EFDATE_LOCLog       : num  6.15 6.24 6.83 6.83 6.83 ...
 $ IndAvgSalaryLog              : num  11.1 11.3 11.1 11.3 11.7 ...
 $ AGELVL_B                     : chr  "1" "0" "0" "0" ...
 $ AGELVL_C                     : chr  "0" "1" "1" "1" ...
 $ AGELVL_D                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_E                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_F                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_G                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_H                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_I                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_J                     : chr  "0" "0" "0" "0" ...
 $ AGELVL_K                     : chr  "0" "0" "0" "0" ...
 $ LOC_01                       : chr  "0" "0" "0" "0" ...
 $ LOC_02                       : chr  "0" "0" "0" "0" ...
 $ LOC_04                       : chr  "0" "0" "0" "0" ...
 $ LOC_05                       : chr  "0" "0" "0" "0" ...
 $ LOC_06                       : chr  "0" "0" "0" "0" ...
 $ LOC_08                       : chr  "0" "0" "0" "0" ...
 $ LOC_09                       : chr  "0" "0" "0" "0" ...
 $ LOC_10                       : chr  "0" "0" "0" "0" ...
 $ LOC_11                       : chr  "0" "0" "0" "0" ...
 $ LOC_12                       : chr  "0" "0" "0" "0" ...
 $ LOC_13                       : chr  "0" "0" "0" "0" ...
 $ LOC_15                       : chr  "0" "0" "0" "0" ...
 $ LOC_16                       : chr  "0" "0" "0" "0" ...
 $ LOC_17                       : chr  "0" "0" "0" "0" ...
 $ LOC_18                       : chr  "0" "0" "0" "0" ...
 $ LOC_19                       : chr  "0" "0" "0" "0" ...
 $ LOC_20                       : chr  "0" "0" "0" "0" ...
 $ LOC_21                       : chr  "0" "0" "0" "0" ...
 $ LOC_22                       : chr  "0" "0" "0" "0" ...
 $ LOC_23                       : chr  "0" "0" "0" "0" ...
 $ LOC_24                       : chr  "0" "0" "1" "1" ...
 $ LOC_25                       : chr  "0" "0" "0" "0" ...
 $ LOC_26                       : chr  "0" "0" "0" "0" ...
 $ LOC_27                       : chr  "0" "0" "0" "0" ...
 $ LOC_28                       : chr  "0" "0" "0" "0" ...
 $ LOC_29                       : chr  "0" "0" "0" "0" ...
 $ LOC_30                       : chr  "0" "0" "0" "0" ...
 $ LOC_31                       : chr  "0" "0" "0" "0" ...
 $ LOC_32                       : chr  "0" "0" "0" "0" ...
 $ LOC_33                       : chr  "0" "0" "0" "0" ...
 $ LOC_34                       : chr  "0" "0" "0" "0" ...
 $ LOC_35                       : chr  "0" "0" "0" "0" ...
 $ LOC_36                       : chr  "0" "0" "0" "0" ...
 $ LOC_37                       : chr  "0" "0" "0" "0" ...
 $ LOC_38                       : chr  "0" "0" "0" "0" ...
 $ LOC_39                       : chr  "0" "0" "0" "0" ...
 $ LOC_40                       : chr  "0" "0" "0" "0" ...
 $ LOC_41                       : chr  "0" "0" "0" "0" ...
 $ LOC_42                       : chr  "1" "0" "0" "0" ...
 $ LOC_44                       : chr  "0" "0" "0" "0" ...
 $ LOC_45                       : chr  "0" "0" "0" "0" ...
 $ LOC_46                       : chr  "0" "0" "0" "0" ...
 $ LOC_47                       : chr  "0" "0" "0" "0" ...
 $ LOC_48                       : chr  "0" "0" "0" "0" ...
 $ LOC_49                       : chr  "0" "1" "0" "0" ...
 $ LOC_50                       : chr  "0" "0" "0" "0" ...
 $ LOC_51                       : chr  "0" "0" "0" "0" ...
 $ LOC_53                       : chr  "0" "0" "0" "0" ...
 $ LOC_54                       : chr  "0" "0" "0" "0" ...
 $ LOC_55                       : chr  "0" "0" "0" "0" ...
 $ LOC_56                       : chr  "0" "0" "0" "0" ...
 $ TOA_10                       : chr  "1" "1" "0" "1" ...
 $ TOA_15                       : chr  "0" "0" "0" "0" ...
 $ TOA_20                       : chr  "0" "0" "0" "0" ...
 $ TOA_30                       : chr  "0" "0" "0" "0" ...
 $ TOA_32                       : chr  "0" "0" "0" "0" ...
 $ TOA_35                       : chr  "0" "0" "0" "0" ...
 $ TOA_38                       : chr  "0" "0" "1" "0" ...
 $ TOA_40                       : chr  "0" "0" "0" "0" ...
 $ TOA_42                       : chr  "0" "0" "0" "0" ...
 $ TOA_44                       : chr  "0" "0" "0" "0" ...
 $ TOA_45                       : chr  "0" "0" "0" "0" ...
 $ TOA_48                       : chr  "0" "0" "0" "0" ...
 $ LOCTYP_1                     : chr  "1" "1" "1" "1" ...
 $ PPTYP_1                      : chr  "1" "1" "1" "1" ...
 $ PPGROUP_11                   : chr  "1" "1" "1" "1" ...
 $ PPGROUP_12                   : chr  "0" "0" "0" "0" ...
 $ TOATYP_1                     : chr  "1" "1" "1" "1" ...
  [list output truncated]
 [1] "GSEGRD"                        "IndAvgSalary"                 
 [3] "SalaryOverUnderIndAvg"         "LowerLimitAge"                
 [5] "YearsToRetirement"             "BLS_FEDERAL_OtherSep_Rate"    
 [7] "BLS_FEDERAL_Quits_Rate"        "BLS_FEDERAL_TotalSep_Level"   
 [9] "BLS_FEDERAL_JobOpenings_Rate"  "BLS_FEDERAL_OtherSep_Level"   
[11] "BLS_FEDERAL_Quits_Level"       "BLS_FEDERAL_JobOpenings_Level"
[13] "BLS_FEDERAL_Layoffs_Rate"      "BLS_FEDERAL_Layoffs_Level"    
[15] "BLS_FEDERAL_TotalSep_Rate"     "SALARYLog"                    
[17] "LOSSqrt"                       "SEPCount_EFDATE_OCCLog"       
[19] "SEPCount_EFDATE_LOCLog"        "IndAvgSalaryLog"              
[21] "AGELVL_B"                      "AGELVL_C"                     
[23] "AGELVL_D"                      "AGELVL_E"                     
[25] "AGELVL_F"                      "AGELVL_G"                     
[27] "AGELVL_H"                      "AGELVL_I"                     
[29] "AGELVL_J"                      "AGELVL_K"                     
[31] "LOC_01"                        "LOC_02"                       
[33] "LOC_04"                        "LOC_05"                       
[35] "LOC_06"                        "LOC_08"                       
[37] "LOC_09"                        "LOC_10"                       
[39] "LOC_11"                        "LOC_12"                       
[41] "LOC_13"                        "LOC_15"                       
[43] "LOC_16"                        "LOC_17"                       
[45] "LOC_18"                        "LOC_19"                       
[47] "LOC_20"                        "LOC_21"                       
[49] "LOC_22"                        "LOC_23"                       
[51] "LOC_24"                        "LOC_25"                       
[53] "LOC_26"                        "LOC_27"                       
[55] "LOC_28"                        "LOC_29"                       
[57] "LOC_30"                        "LOC_31"                       
[59] "LOC_32"                        "LOC_33"                       
[61] "LOC_34"                        "LOC_35"                       
[63] "LOC_36"                        "LOC_37"                       
[65] "LOC_38"                        "LOC_39"                       
[67] "LOC_40"                        "LOC_41"                       
[69] "LOC_42"                        "LOC_44"                       
[71] "LOC_45"                        "LOC_46"                       
[73] "LOC_47"                        "LOC_48"                       
[75] "LOC_49"                        "LOC_50"                       
[77] "LOC_51"                        "LOC_53"                       
[79] "LOC_54"                        "LOC_55"                       
[81] "LOC_56"                        "TOA_10"                       
[83] "TOA_15"                        "TOA_20"                       
[85] "TOA_30"                        "TOA_32"                       
[87] "TOA_35"                        "TOA_38"                       
[89] "TOA_40"                        "TOA_42"                       
[91] "TOA_44"                        "TOA_45"                       
[93] "TOA_48"                        "LOCTYP_1"                     
[95] "PPTYP_1"                       "PPGROUP_11"                   
[97] "PPGROUP_12"                    "TOATYP_1"                     
[99] "TOATYP_2"                     
[1] "# of SEP observations =  14920"
[1] "SEP type =  0" "SEP type =  1"
[1] "SEP class =  integer"
In [128]:
%%R
vars <- names(OPMAnalysisDataNoFamBinary[,22:ncol(OPMAnalysisDataNoFamBinary)])
OPMAnalysisDataNoFamBinary[, vars] <- sapply(OPMAnalysisDataNoFamBinary[,22:ncol(OPMAnalysisDataNoFamBinary)], as.numeric)

print(summary(OPMAnalysisDataNoFamBinary))

# Apply Min/Max scaler function to data
OPMAnalysisDataNoFamBinary[,-1] <- sapply(OPMAnalysisDataNoFamBinary[,-1], function(x) (x-min(x))/(max(x)-min(x)))

cat("\n\n\n")
print(summary(OPMAnalysisDataNoFamBinary))
     SEP                GSEGRD       IndAvgSalary    SalaryOverUnderIndAvg
 Length:14920       Min.   : 7.00   Min.   : 39570   Min.   :-119176.8    
 Class :character   1st Qu.:11.00   1st Qu.: 69859   1st Qu.:  -6284.6    
 Mode  :character   Median :12.00   Median : 85479   Median :   -891.9    
                    Mean   :11.99   Mean   : 93617   Mean   :   -527.3    
                    3rd Qu.:13.00   3rd Qu.:105405   3rd Qu.:   5535.8    
                    Max.   :15.00   Max.   :220807   Max.   : 125012.8    
 LowerLimitAge   YearsToRetirement BLS_FEDERAL_OtherSep_Rate
 Min.   :20.00   Min.   :-8.00     Min.   :0.3000           
 1st Qu.:30.00   1st Qu.: 2.00     1st Qu.:0.4000           
 Median :45.00   Median :12.00     Median :0.4000           
 Mean   :43.62   Mean   :13.38     Mean   :0.4303           
 3rd Qu.:55.00   3rd Qu.:27.00     3rd Qu.:0.5000           
 Max.   :65.00   Max.   :37.00     Max.   :0.6000           
 BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate
 Min.   :0.3000         Min.   :26.00              Min.   :1.900               
 1st Qu.:0.4000         1st Qu.:31.00              1st Qu.:2.200               
 Median :0.5000         Median :34.00              Median :2.300               
 Mean   :0.4571         Mean   :36.41              Mean   :2.458               
 3rd Qu.:0.5000         3rd Qu.:39.00              3rd Qu.:2.800               
 Max.   :0.6000         Max.   :60.00              Max.   :3.200               
 BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level
 Min.   : 8.00              Min.   : 9.00          
 1st Qu.:10.00              1st Qu.:10.00          
 Median :12.00              Median :13.00          
 Mean   :11.98              Mean   :12.08          
 3rd Qu.:14.00              3rd Qu.:13.00          
 Max.   :17.00              Max.   :17.00          
 BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate
 Min.   :55.00                 Min.   :0.3000          
 1st Qu.:62.00                 1st Qu.:0.4000          
 Median :67.00                 Median :0.4000          
 Mean   :69.92                 Mean   :0.4601          
 3rd Qu.:80.00                 3rd Qu.:0.5000          
 Max.   :91.00                 Max.   :1.1000          
 BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate   SALARYLog    
 Min.   : 7.00             Min.   :1.000             Min.   :10.58  
 1st Qu.:10.00             1st Qu.:1.100             1st Qu.:11.16  
 Median :12.00             Median :1.200             Median :11.37  
 Mean   :12.44             Mean   :1.319             Mean   :11.39  
 3rd Qu.:12.00             3rd Qu.:1.400             3rd Qu.:11.60  
 Max.   :30.00             Max.   :2.200             Max.   :12.65  
    LOSSqrt      SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
 Min.   :0.000   Min.   :0.000          Min.   :2.890          Min.   :10.59  
 1st Qu.:1.817   1st Qu.:3.526          1st Qu.:5.740          1st Qu.:11.15  
 Median :2.627   Median :4.407          Median :6.390          Median :11.36  
 Mean   :3.014   Mean   :4.348          Mean   :6.323          Mean   :11.40  
 3rd Qu.:4.159   3rd Qu.:5.481          3rd Qu.:7.024          3rd Qu.:11.57  
 Max.   :8.456   Max.   :6.562          Max.   :7.934          Max.   :12.31  
    AGELVL_B          AGELVL_C         AGELVL_D         AGELVL_E     
 Min.   :0.00000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.00000   Median :0.0000   Median :0.0000   Median :0.0000  
 Mean   :0.01133   Mean   :0.1011   Mean   :0.1509   Mean   :0.1266  
 3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
    AGELVL_F        AGELVL_G         AGELVL_H         AGELVL_I     
 Min.   :0.000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.000   Median :0.0000   Median :0.0000   Median :0.0000  
 Mean   :0.104   Mean   :0.0931   Mean   :0.1007   Mean   :0.1231  
 3rd Qu.:0.000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :1.000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
    AGELVL_J         AGELVL_K           LOC_01            LOC_02        
 Min.   :0.0000   Min.   :0.00000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.0000   Median :0.00000   Median :0.00000   Median :0.000000  
 Mean   :0.1083   Mean   :0.08083   Mean   :0.01769   Mean   :0.007373  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.00000   Max.   :1.000000  
     LOC_04            LOC_05             LOC_06            LOC_08       
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.000000   Median :0.00000   Median :0.00000  
 Mean   :0.02634   Mean   :0.007507   Mean   :0.07688   Mean   :0.03076  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00000   Max.   :1.00000  
     LOC_09             LOC_10            LOC_11          LOC_12       
 Min.   :0.000000   Min.   :0.00000   Min.   :0.000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.000   1st Qu.:0.00000  
 Median :0.000000   Median :0.00000   Median :0.000   Median :0.00000  
 Mean   :0.004156   Mean   :0.00134   Mean   :0.109   Mean   :0.03472  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.000   Max.   :1.00000  
     LOC_13            LOC_15            LOC_16             LOC_17       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.000000   Median :0.00000  
 Mean   :0.03626   Mean   :0.01656   Mean   :0.005764   Mean   :0.02212  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.000000   Max.   :1.00000  
     LOC_18             LOC_19             LOC_20             LOC_21       
 Min.   :0.000000   Min.   :0.000000   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.000000   Median :0.000000   Median :0.000000   Median :0.00000  
 Mean   :0.007909   Mean   :0.004156   Mean   :0.007641   Mean   :0.01059  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.000000   Max.   :1.00000  
     LOC_22            LOC_23            LOC_24            LOC_25       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.01039   Mean   :0.00382   Mean   :0.08231   Mean   :0.01421  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
     LOC_26            LOC_27             LOC_28             LOC_29       
 Min.   :0.00000   Min.   :0.000000   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.00000   Median :0.000000   Median :0.000000   Median :0.00000  
 Mean   :0.01133   Mean   :0.007909   Mean   :0.007708   Mean   :0.01669  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.000000   Max.   :1.00000  
     LOC_30             LOC_31             LOC_32             LOC_33        
 Min.   :0.000000   Min.   :0.000000   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.000000   Median :0.000000   Median :0.000000   Median :0.000000  
 Mean   :0.007976   Mean   :0.003887   Mean   :0.007038   Mean   :0.001474  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.000000   Max.   :1.000000  
     LOC_34            LOC_35            LOC_36           LOC_37       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.0000   Median :0.00000  
 Mean   :0.01086   Mean   :0.02138   Mean   :0.0242   Mean   :0.01924  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
     LOC_38             LOC_39            LOC_40            LOC_41       
 Min.   :0.000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.000000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.003686   Mean   :0.02513   Mean   :0.01649   Mean   :0.01139  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
     LOC_42            LOC_44             LOC_45            LOC_46       
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.000000   Median :0.00000   Median :0.00000  
 Mean   :0.02299   Mean   :0.002078   Mean   :0.00811   Mean   :0.00811  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00000   Max.   :1.00000  
     LOC_47            LOC_48           LOC_49            LOC_50        
 Min.   :0.00000   Min.   :0.0000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.00000   Median :0.0000   Median :0.00000   Median :0.000000  
 Mean   :0.01052   Mean   :0.0628   Mean   :0.01099   Mean   :0.001072  
 3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.00000   Max.   :1.000000  
     LOC_51            LOC_53            LOC_54             LOC_55        
 Min.   :0.00000   Min.   :0.00000   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.00000   Median :0.000000   Median :0.000000  
 Mean   :0.09243   Mean   :0.02983   Mean   :0.006434   Mean   :0.006836  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.000000   Max.   :1.000000  
     LOC_56             TOA_10           TOA_15           TOA_20       
 Min.   :0.000000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.000000   Median :1.0000   Median :0.0000   Median :0.00000  
 Mean   :0.003887   Mean   :0.5944   Mean   :0.1529   Mean   :0.01944  
 3rd Qu.:0.000000   3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
     TOA_30            TOA_32              TOA_35             TOA_38      
 Min.   :0.00000   Min.   :0.0000000   Min.   :0.000000   Min.   :0.0000  
 1st Qu.:0.00000   1st Qu.:0.0000000   1st Qu.:0.000000   1st Qu.:0.0000  
 Median :0.00000   Median :0.0000000   Median :0.000000   Median :0.0000  
 Mean   :0.06635   Mean   :0.0007373   Mean   :0.005496   Mean   :0.1244  
 3rd Qu.:0.00000   3rd Qu.:0.0000000   3rd Qu.:0.000000   3rd Qu.:0.0000  
 Max.   :1.00000   Max.   :1.0000000   Max.   :1.000000   Max.   :1.0000  
     TOA_40            TOA_42             TOA_44              TOA_45        
 Min.   :0.00000   Min.   :0.000000   Min.   :0.0000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.0000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.000000   Median :0.0000000   Median :0.000000  
 Mean   :0.01367   Mean   :0.004156   Mean   :0.0006702   Mean   :0.000134  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.0000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.0000000   Max.   :1.000000  
     TOA_48           LOCTYP_1    PPTYP_1    PPGROUP_11       PPGROUP_12     
 Min.   :0.00000   Min.   :1   Min.   :1   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:1   1st Qu.:1   1st Qu.:1.0000   1st Qu.:0.00000  
 Median :0.00000   Median :1   Median :1   Median :1.0000   Median :0.00000  
 Mean   :0.01769   Mean   :1   Mean   :1   Mean   :0.9641   Mean   :0.03592  
 3rd Qu.:0.00000   3rd Qu.:1   3rd Qu.:1   3rd Qu.:1.0000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1   Max.   :1   Max.   :1.0000   Max.   :1.00000  
    TOATYP_1         TOATYP_2      
 Min.   :0.0000   Min.   :0.00000  
 1st Qu.:1.0000   1st Qu.:0.00000  
 Median :1.0000   Median :0.00000  
 Mean   :0.9442   Mean   :0.05576  
 3rd Qu.:1.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000  



     SEP                GSEGRD        IndAvgSalary    SalaryOverUnderIndAvg
 Length:14920       Min.   :0.0000   Min.   :0.0000   Min.   :0.0000       
 Class :character   1st Qu.:0.5000   1st Qu.:0.1671   1st Qu.:0.4623       
 Mode  :character   Median :0.6250   Median :0.2533   Median :0.4844       
                    Mean   :0.6234   Mean   :0.2982   Mean   :0.4859       
                    3rd Qu.:0.7500   3rd Qu.:0.3633   3rd Qu.:0.5107       
                    Max.   :1.0000   Max.   :1.0000   Max.   :1.0000       
                                                                           
 LowerLimitAge    YearsToRetirement BLS_FEDERAL_OtherSep_Rate
 Min.   :0.0000   Min.   :0.0000    Min.   :0.0000           
 1st Qu.:0.2222   1st Qu.:0.2222    1st Qu.:0.3333           
 Median :0.5556   Median :0.4444    Median :0.3333           
 Mean   :0.5249   Mean   :0.4751    Mean   :0.4345           
 3rd Qu.:0.7778   3rd Qu.:0.7778    3rd Qu.:0.6667           
 Max.   :1.0000   Max.   :1.0000    Max.   :1.0000           
                                                             
 BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate
 Min.   :0.0000         Min.   :0.0000             Min.   :0.0000              
 1st Qu.:0.3333         1st Qu.:0.1471             1st Qu.:0.2308              
 Median :0.6667         Median :0.2353             Median :0.3077              
 Mean   :0.5238         Mean   :0.3063             Mean   :0.4289              
 3rd Qu.:0.6667         3rd Qu.:0.3824             3rd Qu.:0.6923              
 Max.   :1.0000         Max.   :1.0000             Max.   :1.0000              
                                                                               
 BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level
 Min.   :0.0000             Min.   :0.0000         
 1st Qu.:0.2222             1st Qu.:0.1250         
 Median :0.4444             Median :0.5000         
 Mean   :0.4425             Mean   :0.3852         
 3rd Qu.:0.6667             3rd Qu.:0.5000         
 Max.   :1.0000             Max.   :1.0000         
                                                   
 BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate
 Min.   :0.0000                Min.   :0.0000          
 1st Qu.:0.1944                1st Qu.:0.1250          
 Median :0.3333                Median :0.1250          
 Mean   :0.4144                Mean   :0.2001          
 3rd Qu.:0.6944                3rd Qu.:0.2500          
 Max.   :1.0000                Max.   :1.0000          
                                                       
 BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate   SALARYLog     
 Min.   :0.0000            Min.   :0.00000           Min.   :0.0000  
 1st Qu.:0.1304            1st Qu.:0.08333           1st Qu.:0.2814  
 Median :0.2174            Median :0.16667           Median :0.3826  
 Mean   :0.2366            Mean   :0.26583           Mean   :0.3922  
 3rd Qu.:0.2174            3rd Qu.:0.33333           3rd Qu.:0.4955  
 Max.   :1.0000            Max.   :1.00000           Max.   :1.0000  
                                                                     
    LOSSqrt       SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog
 Min.   :0.0000   Min.   :0.0000         Min.   :0.0000        
 1st Qu.:0.2148   1st Qu.:0.5374         1st Qu.:0.5649        
 Median :0.3107   Median :0.6715         Median :0.6939        
 Mean   :0.3565   Mean   :0.6626         Mean   :0.6806        
 3rd Qu.:0.4919   3rd Qu.:0.8352         3rd Qu.:0.8195        
 Max.   :1.0000   Max.   :1.0000         Max.   :1.0000        
                                                               
 IndAvgSalaryLog     AGELVL_B          AGELVL_C         AGELVL_D     
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.3306   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.4480   Median :0.00000   Median :0.0000   Median :0.0000  
 Mean   :0.4729   Mean   :0.01133   Mean   :0.1011   Mean   :0.1509  
 3rd Qu.:0.5699   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.0000  
                                                                     
    AGELVL_E         AGELVL_F        AGELVL_G         AGELVL_H     
 Min.   :0.0000   Min.   :0.000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.0000   Median :0.000   Median :0.0000   Median :0.0000  
 Mean   :0.1266   Mean   :0.104   Mean   :0.0931   Mean   :0.1007  
 3rd Qu.:0.0000   3rd Qu.:0.000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :1.0000   Max.   :1.000   Max.   :1.0000   Max.   :1.0000  
                                                                   
    AGELVL_I         AGELVL_J         AGELVL_K           LOC_01       
 Min.   :0.0000   Min.   :0.0000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.0000   Median :0.0000   Median :0.00000   Median :0.00000  
 Mean   :0.1231   Mean   :0.1083   Mean   :0.08083   Mean   :0.01769  
 3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.00000   Max.   :1.00000  
                                                                      
     LOC_02             LOC_04            LOC_05             LOC_06       
 Min.   :0.000000   Min.   :0.00000   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.000000   Median :0.00000   Median :0.000000   Median :0.00000  
 Mean   :0.007373   Mean   :0.02634   Mean   :0.007507   Mean   :0.07688  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.000000   Max.   :1.00000  
                                                                          
     LOC_08            LOC_09             LOC_10            LOC_11     
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00000   Min.   :0.000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.000  
 Median :0.00000   Median :0.000000   Median :0.00000   Median :0.000  
 Mean   :0.03076   Mean   :0.004156   Mean   :0.00134   Mean   :0.109  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00000   Max.   :1.000  
                                                                       
     LOC_12            LOC_13            LOC_15            LOC_16        
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.000000  
 Mean   :0.03472   Mean   :0.03626   Mean   :0.01656   Mean   :0.005764  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.000000  
                                                                         
     LOC_17            LOC_18             LOC_19             LOC_20        
 Min.   :0.00000   Min.   :0.000000   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.000000   Median :0.000000   Median :0.000000  
 Mean   :0.02212   Mean   :0.007909   Mean   :0.004156   Mean   :0.007641  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.000000   Max.   :1.000000  
                                                                           
     LOC_21            LOC_22            LOC_23            LOC_24       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.01059   Mean   :0.01039   Mean   :0.00382   Mean   :0.08231  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                        
     LOC_25            LOC_26            LOC_27             LOC_28        
 Min.   :0.00000   Min.   :0.00000   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.00000   Median :0.000000   Median :0.000000  
 Mean   :0.01421   Mean   :0.01133   Mean   :0.007909   Mean   :0.007708  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.000000   Max.   :1.000000  
                                                                          
     LOC_29            LOC_30             LOC_31             LOC_32        
 Min.   :0.00000   Min.   :0.000000   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.000000   Median :0.000000   Median :0.000000  
 Mean   :0.01669   Mean   :0.007976   Mean   :0.003887   Mean   :0.007038  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.000000   Max.   :1.000000  
                                                                           
     LOC_33             LOC_34            LOC_35            LOC_36      
 Min.   :0.000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.0000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000  
 Median :0.000000   Median :0.00000   Median :0.00000   Median :0.0000  
 Mean   :0.001474   Mean   :0.01086   Mean   :0.02138   Mean   :0.0242  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.0000  
                                                                        
     LOC_37            LOC_38             LOC_39            LOC_40       
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.000000   Median :0.00000   Median :0.00000  
 Mean   :0.01924   Mean   :0.003686   Mean   :0.02513   Mean   :0.01649  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00000   Max.   :1.00000  
                                                                         
     LOC_41            LOC_42            LOC_44             LOC_45       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.000000   Median :0.00000  
 Mean   :0.01139   Mean   :0.02299   Mean   :0.002078   Mean   :0.00811  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.000000   Max.   :1.00000  
                                                                         
     LOC_46            LOC_47            LOC_48           LOC_49       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.0000   Median :0.00000  
 Mean   :0.00811   Mean   :0.01052   Mean   :0.0628   Mean   :0.01099  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
                                                                       
     LOC_50             LOC_51            LOC_53            LOC_54        
 Min.   :0.000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.000000   Median :0.00000   Median :0.00000   Median :0.000000  
 Mean   :0.001072   Mean   :0.09243   Mean   :0.02983   Mean   :0.006434  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.000000  
                                                                          
     LOC_55             LOC_56             TOA_10           TOA_15      
 Min.   :0.000000   Min.   :0.000000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.000000   Median :0.000000   Median :1.0000   Median :0.0000  
 Mean   :0.006836   Mean   :0.003887   Mean   :0.5944   Mean   :0.1529  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:1.0000   3rd Qu.:0.0000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.0000   Max.   :1.0000  
                                                                        
     TOA_20            TOA_30            TOA_32              TOA_35        
 Min.   :0.00000   Min.   :0.00000   Min.   :0.0000000   Min.   :0.000000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000000   1st Qu.:0.000000  
 Median :0.00000   Median :0.00000   Median :0.0000000   Median :0.000000  
 Mean   :0.01944   Mean   :0.06635   Mean   :0.0007373   Mean   :0.005496  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000000   3rd Qu.:0.000000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.0000000   Max.   :1.000000  
                                                                           
     TOA_38           TOA_40            TOA_42             TOA_44         
 Min.   :0.0000   Min.   :0.00000   Min.   :0.000000   Min.   :0.0000000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.0000000  
 Median :0.0000   Median :0.00000   Median :0.000000   Median :0.0000000  
 Mean   :0.1244   Mean   :0.01367   Mean   :0.004156   Mean   :0.0006702  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.0000000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.000000   Max.   :1.0000000  
                                                                          
     TOA_45             TOA_48           LOCTYP_1        PPTYP_1     
 Min.   :0.000000   Min.   :0.00000   Min.   : NA     Min.   : NA    
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.: NA     1st Qu.: NA    
 Median :0.000000   Median :0.00000   Median : NA     Median : NA    
 Mean   :0.000134   Mean   :0.01769   Mean   :NaN     Mean   :NaN    
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.: NA     3rd Qu.: NA    
 Max.   :1.000000   Max.   :1.00000   Max.   : NA     Max.   : NA    
                                      NA's   :14920   NA's   :14920  
   PPGROUP_11       PPGROUP_12         TOATYP_1         TOATYP_2      
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:1.0000   1st Qu.:0.00000   1st Qu.:1.0000   1st Qu.:0.00000  
 Median :1.0000   Median :0.00000   Median :1.0000   Median :0.00000  
 Mean   :0.9641   Mean   :0.03592   Mean   :0.9442   Mean   :0.05576  
 3rd Qu.:1.0000   3rd Qu.:0.00000   3rd Qu.:1.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
                                                                      

LOCTYP_1 and PPTYP_1 have only single level and need removed:

In [129]:
%R sapply(OPMAnalysisDataNoFamBinary, function(x) length(unique(x[!is.na(x)])))
Out[129]:
array([    2,     9,  1878, 10766,    10,    10,     4,     4,     9,
          10,     8,     6,    10,     4,     7,     6,  3537,   466,
         180,   399,  1878,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     0,     0,     2,     2,     2,     2], dtype=int32)

Unique counts with LOCTYP_1 and PPTYP_1 removed:

In [130]:
%R sapply(OPMAnalysisDataNoFamBinary[,-c(95,96)], function(x) length(unique(x[!is.na(x)])))
Out[130]:
array([    2,     9,  1878, 10766,    10,    10,     4,     4,     9,
          10,     8,     6,    10,     4,     7,     6,  3537,   466,
         180,   399,  1878,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2,     2,
           2,     2,     2,     2,     2,     2,     2,     2], dtype=int32)

Data example with LOCTYP_1 and PPTYP_1 removed:

In [131]:
%R OPMAnalysisDataNoFamBinary[,-c(95,96)]
Out[131]:
SEP GSEGRD IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog AGELVL_B AGELVL_C AGELVL_D AGELVL_E AGELVL_F AGELVL_G AGELVL_H AGELVL_I AGELVL_J AGELVL_K LOC_01 LOC_02 LOC_04 LOC_05 LOC_06 LOC_08 LOC_09 LOC_10 LOC_11 LOC_12 LOC_13 LOC_15 LOC_16 LOC_17 LOC_18 LOC_19 LOC_20 LOC_21 LOC_22 LOC_23 LOC_24 LOC_25 LOC_26 LOC_27 LOC_28 LOC_29 LOC_30 LOC_31 LOC_32 LOC_33 LOC_34 LOC_35 LOC_36 LOC_37 LOC_38 LOC_39 LOC_40 LOC_41 LOC_42 LOC_44 LOC_45 LOC_46 LOC_47 LOC_48 LOC_49 LOC_50 LOC_51 LOC_53 LOC_54 LOC_55 LOC_56 TOA_10 TOA_15 TOA_20 TOA_30 TOA_32 TOA_35 TOA_38 TOA_40 TOA_42 TOA_44 TOA_45 TOA_48 PPGROUP_11 PPGROUP_12 TOATYP_1 TOATYP_2
0 NS 0.500 0.145270 0.471501 0.000000 1.000000 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.220405 0.256387 0.886424 0.646808 0.296670 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
1 NS 0.625 0.229804 0.449531 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.292431 0.317332 0.886424 0.664165 0.418258 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
2 NS 0.500 0.145270 0.476554 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.229938 0.236525 0.886424 0.780616 0.296670 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
3 NS 0.625 0.235042 0.461238 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.317367 0.284814 0.657909 0.780616 0.425018 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
4 NS 0.750 0.454481 0.381495 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.432120 0.167248 0.631340 0.780616 0.654628 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
5 NS 0.625 0.259816 0.463495 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.348513 0.272261 0.477794 0.833639 0.455963 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
6 NS 0.750 0.346492 0.437093 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.400972 0.261785 0.507769 0.780616 0.552865 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
7 NS 0.750 0.279108 0.487106 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.400972 0.118262 0.245250 0.833639 0.478971 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
8 NS 0.500 0.130758 0.487325 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.229938 0.171379 0.507769 0.819148 0.272980 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
9 NS 0.500 0.159907 0.501826 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.293116 0.269680 0.636103 0.441657 0.319626 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
10 NS 0.625 0.416223 0.440878 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.468766 0.230536 0.631340 0.780616 0.620575 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
11 NS 0.625 0.233497 0.472709 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.245234 0.507769 0.780616 0.423032 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
12 NS 0.500 0.145270 0.473020 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.223291 0.083624 0.886424 0.754968 0.296670 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
13 NS 0.625 0.237847 0.469480 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.242366 0.676981 0.819148 0.428605 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14 NS 0.250 0.071125 0.469468 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.097226 0.190693 0.886424 0.658679 0.164022 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
15 NS 0.500 0.157737 0.482232 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.257052 0.245234 0.613392 0.926453 0.316279 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
16 NS 0.500 0.176611 0.440286 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.124035 0.167409 0.693898 0.344763 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
17 NS 0.500 0.133486 0.480288 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.220483 0.179354 0.676981 0.758602 0.277507 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
18 NS 0.250 0.071125 0.469468 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.097226 0.274817 0.886424 0.554465 0.164022 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
19 NS 0.625 0.229804 0.473607 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.330407 0.321709 0.886424 0.512729 0.418258 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
20 NS 0.000 0.033480 0.483862 0.111111 0.888889 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.062706 0.124035 0.636103 0.664165 0.082982 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
21 NS 0.375 0.167432 0.508621 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.312989 0.239463 0.983178 0.530187 0.331085 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
22 NS 0.625 0.229804 0.471068 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.326540 0.163014 0.886424 0.754968 0.418258 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
23 NS 0.625 0.235042 0.487541 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.356711 0.397545 0.657909 0.754968 0.425018 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
24 NS 1.000 0.584177 0.421367 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.575726 0.303822 0.507769 0.833639 0.757160 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
25 NS 0.750 0.324995 0.465326 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.416800 0.269680 0.886424 0.780616 0.530294 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
26 NS 0.500 0.145270 0.461873 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.201704 0.194325 0.886424 0.575489 0.296670 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
27 NS 0.750 0.324995 0.489873 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.446969 0.370220 0.886424 0.833639 0.530294 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
28 NS 0.875 0.457466 0.436141 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.497367 0.360652 0.788753 0.833639 0.657203 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
29 NS 0.625 0.261259 0.468083 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.356711 0.306115 0.334818 0.754968 0.457717 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
30 NS 0.250 0.071125 0.500997 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.169128 0.204837 0.886424 0.833639 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
31 NS 0.500 0.159810 0.452756 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.179354 0.541772 0.926453 0.319477 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
32 NS 0.625 0.229804 0.470245 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.325280 0.289683 0.886424 0.926453 0.418258 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
33 NS 0.500 0.167887 0.502583 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.303939 0.390445 0.463931 0.795109 0.331770 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
34 NS 1.000 0.600663 0.426196 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.591050 0.292087 0.788753 0.833639 0.768988 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
35 NS 0.625 0.229804 0.465126 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.317367 0.253645 0.886424 0.780616 0.418258 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
36 NS 0.625 0.259235 0.475118 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.364594 0.431293 0.636103 0.764280 0.455257 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
37 NS 0.500 0.145270 0.511007 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.290350 0.245234 0.886424 0.819148 0.296670 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
38 NS 0.875 0.457466 0.436141 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.497367 0.197891 0.788753 0.833639 0.657203 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
39 NS 0.250 0.071125 0.491816 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.149272 0.294471 0.886424 0.926453 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
40 NS 0.250 0.071125 0.476008 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.113047 0.282348 0.886424 0.693898 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
41 NS 0.500 0.145270 0.503763 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.278260 0.230536 0.886424 0.669127 0.296670 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
42 NS 0.625 0.237847 0.490124 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.363365 0.299183 0.676981 0.819148 0.428605 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
43 NS 0.625 0.229804 0.483865 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.345722 0.375844 0.886424 0.575489 0.418258 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
44 NS 0.250 0.071125 0.493536 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.153054 0.356753 0.886424 0.485185 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
45 NS 0.875 0.442817 0.519531 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.569525 0.167248 0.886424 0.833639 0.644456 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
46 NS 0.500 0.145270 0.494839 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.262939 0.233550 0.886424 0.669127 0.296670 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
47 NS 0.250 0.071125 0.516145 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.200201 0.312893 0.886424 0.530187 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
48 NS 0.250 0.071125 0.500997 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.169128 0.175412 0.886424 0.780616 0.164022 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
49 NS 0.625 0.229804 0.527057 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.405353 0.204837 0.886424 0.819148 0.418258 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
50 NS 0.875 0.457466 0.436141 0.222222 0.777778 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.497367 0.167248 0.788753 0.833639 0.657203 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
51 NS 0.625 0.229804 0.475450 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.277350 0.886424 0.833639 0.418258 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
52 NS 0.875 0.457466 0.465147 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.306115 0.788753 0.819148 0.657203 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
53 NS 1.000 0.600663 0.426196 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.591050 0.230536 0.788753 0.819148 0.768988 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
54 NS 0.750 0.335869 0.469529 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.432120 0.375844 0.657909 0.833639 0.541820 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
55 NS 0.500 0.152993 0.457815 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.149592 0.719015 0.646808 0.308896 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
56 NS 0.750 0.324995 0.465326 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.416800 0.385035 0.886424 0.833639 0.530294 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
57 NS 0.625 0.237847 0.469480 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.253645 0.676981 0.819148 0.428605 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
58 NS 0.750 0.357167 0.490545 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.475367 0.354787 0.719015 0.819148 0.563755 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
59 NS 0.750 0.334077 0.495409 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461379 0.429669 0.507769 0.819148 0.539937 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
60 NS 0.250 0.076243 0.518551 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.212372 0.064775 0.676981 0.819148 0.174217 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
61 NS 0.750 0.324995 0.477599 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.432120 0.274817 0.886424 0.833639 0.530294 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
62 NS 0.500 0.141806 0.524184 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.307522 0.383214 0.673353 0.646808 0.291103 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
63 NS 1.000 0.600663 0.494442 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.647892 0.370220 0.788753 0.833639 0.768988 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
64 NS 0.750 0.358257 0.464461 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.446104 0.397545 0.636103 0.758602 0.564856 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
65 NS 0.625 0.255315 0.479571 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.366771 0.224387 0.350873 0.568726 0.450463 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
66 NS 0.875 0.466374 0.444035 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.512689 0.370220 0.676981 0.833639 0.664820 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
67 NS 0.875 0.457466 0.465147 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.269680 0.788753 0.833639 0.657203 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
68 NS 0.500 0.161323 0.543423 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.358385 0.303822 0.633740 0.530187 0.321800 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
69 NS 0.625 0.229804 0.475450 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.236525 0.886424 0.780616 0.418258 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
70 NS 0.625 0.247738 0.472459 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.348513 0.167248 0.678763 0.780616 0.441082 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
71 NS 0.875 0.457466 0.465147 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.344791 0.788753 0.833639 0.657203 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
72 NS 0.875 0.460397 0.462972 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.299183 0.886424 0.819148 0.659720 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
73 NS 0.500 0.145270 0.567246 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.375138 0.358708 0.886424 0.554465 0.296670 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
74 NS 0.625 0.249762 0.512244 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.405353 0.368326 0.569555 0.780616 0.443603 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
75 NS 0.750 0.324995 0.453049 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.400972 0.264443 0.886424 0.833639 0.530294 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
76 NS 0.750 0.345387 0.475778 0.333333 0.666667 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.448206 0.458029 0.676981 0.631465 0.551725 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
77 NS 0.750 0.350888 0.469405 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.445478 0.279860 0.273032 0.658679 0.557375 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
78 NS 0.875 0.442817 0.476020 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.540655 0.886424 0.780616 0.644456 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
79 NS 0.375 0.167432 0.470777 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.249472 0.211554 0.983178 0.758602 0.331085 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
80 NS 0.625 0.229804 0.474950 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.332440 0.261785 0.886424 0.485185 0.418258 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
81 NS 0.875 0.465612 0.531619 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.595615 0.385035 0.507769 0.819148 0.664172 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
82 NS 0.500 0.156838 0.462874 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.220327 0.336581 0.350873 0.754968 0.314887 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
83 NS 0.750 0.335869 0.444978 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.400972 0.414763 0.657909 0.833639 0.541820 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
84 NS 0.750 0.324995 0.465326 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.416800 0.179354 0.886424 0.780616 0.530294 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
85 NS 0.250 0.090409 0.474620 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.142865 0.362586 0.633740 0.592286 0.201536 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
86 NS 0.750 0.342863 0.443808 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.406214 0.467099 0.726968 0.541266 0.549116 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
87 NS 0.875 0.446712 0.516640 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.569525 0.315120 0.502227 0.833639 0.647872 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
88 NS 0.625 0.235042 0.474101 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.337008 0.397545 0.657909 0.600979 0.425018 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
89 NS 0.375 0.167432 0.539949 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.359901 0.370220 0.983178 0.485185 0.331085 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
90 NS 0.500 0.145270 0.480825 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.237851 0.453425 0.886424 0.926453 0.296670 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
91 NS 0.375 0.167432 0.546075 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.368565 0.358708 0.983178 0.487082 0.331085 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
92 NS 0.500 0.145270 0.468507 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.214668 0.370220 0.886424 0.758602 0.296670 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
93 NS 0.250 0.071125 0.476008 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.113047 0.406245 0.886424 0.819148 0.164022 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
94 NS 0.875 0.457466 0.523159 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.582744 0.312893 0.788753 0.833639 0.657203 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
95 NS 0.750 0.324995 0.514423 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.475367 0.462586 0.886424 0.819148 0.530294 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
96 NS 0.500 0.159907 0.492256 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.277172 0.245234 0.636103 0.396523 0.319626 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
97 NS 0.375 0.151978 0.453008 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.193945 0.236525 0.596123 0.658679 0.307303 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
98 NS 0.625 0.229804 0.499550 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.368238 0.388650 0.886424 0.646808 0.418258 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
99 NS 0.625 0.235042 0.471562 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.333194 0.154195 0.657909 0.819148 0.425018 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
100 NS 0.625 0.245093 0.463603 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.332440 0.282348 0.726968 0.485185 0.437771 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
101 NS 0.500 0.164765 0.449078 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.183211 0.678763 0.669127 0.327050 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
102 NS 0.250 0.078058 0.545670 0.444444 0.555556 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.264586 0.233550 0.378656 0.780616 0.177789 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
103 NS 0.500 0.148902 0.499694 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.275936 0.414763 0.657909 0.780616 0.302452 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
104 NS 0.625 0.229804 0.487477 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.351001 0.386847 0.886424 0.580856 0.418258 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
105 NS 0.625 0.229804 0.477989 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.337008 0.379547 0.886424 0.754968 0.418258 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
106 NS 0.625 0.230087 0.582005 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.472145 0.317332 0.671506 0.926453 0.418626 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
107 NS 0.625 0.229804 0.506413 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.377769 0.323875 0.886424 0.819148 0.418258 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
108 NS 0.500 0.160444 0.491858 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.277172 0.439326 0.350873 0.541266 0.320451 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
109 NS 0.625 0.251906 0.556200 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461151 0.279860 0.719015 0.669127 0.446261 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
110 NS 0.500 0.133486 0.472294 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.134840 0.676981 0.566208 0.277507 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
111 NS 0.500 0.165634 0.495921 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.290397 0.585369 0.726968 0.689525 0.328368 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
112 NS 0.750 0.329759 0.430970 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.376037 0.296836 0.365397 0.819148 0.535372 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
113 NS 0.750 0.382560 0.461002 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.463199 0.370220 0.440441 0.530187 0.588869 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
114 NS 1.000 0.964652 0.429078 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.786755 0.064775 0.863463 0.592286 0.982874 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
115 NS 0.750 0.346492 0.510745 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.488965 0.464095 0.507769 0.780616 0.552865 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
116 NS 0.500 0.161323 0.514949 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.315801 0.594850 0.633740 0.450934 0.321800 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
117 NS 0.875 0.442817 0.476020 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.360652 0.886424 0.833639 0.644456 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
118 NS 0.750 0.324995 0.502150 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461379 0.560968 0.886424 0.819148 0.530294 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
119 NS 0.500 0.216328 0.525207 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.389711 0.158666 0.983178 0.926453 0.400500 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
120 NS 0.875 0.467299 0.428843 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.497367 0.296836 0.273032 0.819148 0.665605 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
121 NS 0.875 0.798539 0.393486 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.682677 0.301511 0.863463 0.764280 0.894864 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
122 NS 0.750 0.357167 0.500878 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.388650 0.719015 0.568726 0.563755 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
123 NS 0.625 0.251906 0.452100 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.322604 0.383214 0.719015 0.754968 0.446261 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
124 NS 0.875 0.798539 0.480668 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.742542 0.264443 0.863463 0.501640 0.894864 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
125 NS 0.875 0.476079 0.422326 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.497367 0.242366 0.477794 0.833639 0.673006 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
126 NS 0.875 0.457722 0.464957 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527536 0.344791 0.657909 0.819148 0.657423 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
127 NS 0.500 0.145270 0.495208 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.263581 0.458029 0.886424 0.487082 0.296670 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
128 NS 0.750 0.360621 0.498314 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.621300 0.565882 0.631465 0.567236 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
129 NS 1.000 0.595495 0.527382 0.555556 0.444444 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.544521 0.633740 0.780616 0.765306 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
130 NS 0.750 0.358674 0.477154 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461379 0.221249 0.673353 0.780616 0.565276 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
131 NS 0.500 0.161323 0.514949 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.315801 0.570854 0.633740 0.501640 0.321800 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
132 NS 0.625 0.254900 0.468851 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.351001 0.201394 0.471020 0.758602 0.449953 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
133 NS 0.750 0.357167 0.500878 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.598366 0.719015 0.473403 0.563755 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
134 NS 0.625 0.229804 0.468504 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.322604 0.615647 0.886424 0.446350 0.418258 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
135 NS 0.875 0.465612 0.546120 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.608148 0.432912 0.507769 0.819148 0.664172 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
136 NS 0.625 0.237847 0.462535 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.322604 0.358708 0.676981 0.795109 0.428605 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
137 NS 0.625 0.229804 0.506446 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.377814 0.617914 0.886424 0.693898 0.418258 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
138 NS 0.625 0.416223 0.507351 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.539085 0.545804 0.631340 0.658679 0.620575 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
139 NS 0.500 0.159907 0.536631 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.347026 0.230536 0.636103 0.441657 0.319626 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
140 NS 1.000 0.601089 0.523230 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.567167 0.719015 0.669127 0.769291 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
141 NS 0.875 0.472886 0.540721 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.608148 0.599534 0.719015 0.780616 0.670325 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
142 NS 0.625 0.229804 0.477989 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.337008 0.277350 0.886424 0.693898 0.418258 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
143 NS 0.750 0.363255 0.522854 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.515059 0.424758 0.390853 0.833639 0.569876 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
144 NS 0.750 0.361582 0.511012 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.501328 0.598366 0.983178 0.592286 0.568200 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
145 NS 0.875 0.442817 0.519531 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.569525 0.697648 0.886424 0.833639 0.644456 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
146 NS 0.875 0.442817 0.462342 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.513544 0.547084 0.886424 0.795109 0.644456 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
147 NS 0.875 0.798539 0.507000 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.759254 0.326027 0.863463 0.689525 0.894864 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
148 NS 0.750 0.324995 0.542709 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.506139 0.383214 0.886424 0.441657 0.530294 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
149 NS 0.750 0.328344 0.499705 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461426 0.650981 0.537355 0.646808 0.533869 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
150 NS 0.750 0.316423 0.463432 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.406214 0.272261 0.671506 0.795109 0.521045 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
151 NS 0.750 0.338426 0.467630 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.432120 0.456499 0.633740 0.833639 0.544499 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
152 NS 0.750 0.358257 0.500068 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.572078 0.636103 0.758602 0.564856 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
153 NS 0.625 0.237847 0.479800 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.348513 0.239463 0.676981 0.819148 0.428605 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
154 NS 1.000 0.601089 0.391754 0.666667 0.333333 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.559903 0.274817 0.719015 0.833639 0.769291 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
155 NS 0.500 0.160444 0.515601 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.315801 0.678336 0.350873 0.795109 0.320451 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
156 NS 0.625 0.233812 0.522444 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.567167 0.448680 0.664165 0.423438 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
157 NS 0.625 0.259235 0.488640 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.383334 0.728059 0.636103 0.575489 0.455257 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
158 NS 1.000 0.587164 0.533566 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.727098 0.657909 0.833639 0.759321 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
159 NS 0.625 0.250681 0.513970 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.408470 0.681422 0.673353 0.631465 0.444744 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
160 NS 0.750 0.358674 0.550453 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.539468 0.679366 0.673353 0.780616 0.565276 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
161 NS 0.875 0.442817 0.461519 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.512689 0.397545 0.886424 0.833639 0.644456 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
162 NS 1.000 0.570844 0.533638 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.661117 0.720334 0.378656 0.780616 0.747416 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
163 NS 0.625 0.253307 0.564771 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.472145 0.373979 0.596123 0.926453 0.447991 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
164 NS 0.625 0.237847 0.536076 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.424436 0.667947 0.676981 0.631465 0.428605 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
165 NS 0.625 0.235042 0.517125 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.397438 0.634663 0.657909 0.926453 0.425018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
166 NS 0.625 0.243253 0.536437 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.429866 0.528886 0.613392 0.926453 0.435458 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
167 NS 1.000 0.647031 0.489132 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.721304 0.507769 0.780616 0.801021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
168 NS 1.000 0.593744 0.516641 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.661117 0.688568 0.676981 0.833639 0.764054 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
169 NS 0.750 0.359389 0.487946 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.474298 0.686534 0.613392 0.600979 0.565996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
170 NS 0.500 0.157829 0.470054 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.235180 0.423109 0.569555 0.403840 0.316422 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
171 NS 0.625 0.235042 0.547175 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.435564 0.658458 0.657909 0.926453 0.425018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
172 NS 0.625 0.242062 0.523938 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.413056 0.672122 0.296522 0.485185 0.433955 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
173 NS 0.875 0.465612 0.474786 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.543096 0.721304 0.507769 0.490823 0.664172 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
174 NS 0.750 0.358674 0.579173 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.566921 0.681422 0.673353 0.780616 0.565276 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
175 NS 0.750 0.333535 0.518417 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.528886 0.665826 0.568726 0.539366 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
176 NS 1.000 0.580642 0.492241 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.634297 0.348824 0.665826 0.780616 0.754592 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
177 NS 0.750 0.357167 0.530359 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.518138 0.658458 0.719015 0.758602 0.563755 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
178 NS 0.500 0.145270 0.500810 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.273244 0.712525 0.886424 0.819148 0.296670 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
179 NS 0.625 0.229804 0.515673 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.390336 0.695641 0.886424 0.575489 0.418258 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
180 NS 0.750 0.335869 0.555454 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527593 0.714485 0.657909 0.833639 0.541820 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
181 NS 0.500 0.159907 0.498022 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.286841 0.498950 0.636103 0.819148 0.319626 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
182 NS 0.625 0.247066 0.532264 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.428205 0.562213 0.245250 0.430673 0.440243 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
183 NS 0.750 0.324995 0.493984 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.451843 0.436130 0.886424 0.646808 0.530294 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
184 NS 0.625 0.229804 0.473607 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.330407 0.230536 0.886424 0.512729 0.418258 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
185 NS 0.500 0.157119 0.454753 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.205010 0.470084 0.671506 0.658679 0.315323 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
186 NS 1.000 0.600663 0.523547 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.459553 0.788753 0.833639 0.768988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
187 NS 0.625 0.235042 0.493074 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.364594 0.672122 0.657909 0.494495 0.425018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
188 NS 0.500 0.161323 0.520551 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.324482 0.642330 0.633740 0.631465 0.321800 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
189 NS 0.750 0.324995 0.468348 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.420618 0.647750 0.886424 0.541266 0.530294 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
190 NS 1.000 0.580887 0.530562 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.664452 0.719363 0.886424 0.646808 0.754770 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
191 NS 0.625 0.287877 0.469663 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.386425 0.342757 0.983178 0.754968 0.489136 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
192 NS 0.750 0.360196 0.537398 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.527593 0.648829 0.350873 0.780616 0.566808 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
193 NS 0.875 0.472886 0.571861 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.634009 0.664799 0.719015 0.819148 0.670325 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
194 NS 0.375 0.164726 0.484707 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.270396 0.409673 0.678763 0.689525 0.326990 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
195 NS 1.000 0.600663 0.552111 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.691200 0.616782 0.788753 0.819148 0.768988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
196 NS 1.000 0.600663 0.477484 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.634380 0.647750 0.788753 0.693898 0.768988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
197 NS 0.750 0.361582 0.515509 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.506091 0.277350 0.983178 0.819148 0.568200 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
198 NS 0.625 0.249482 0.510814 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.703637 0.350873 0.473403 0.443255 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
199 NS 0.625 0.416223 0.540588 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.570742 0.673162 0.631340 0.819148 0.620575 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
200 NS 1.000 0.601135 0.485414 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.641025 0.720334 0.211247 0.512729 0.769323 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
201 NS 1.000 0.587164 0.453460 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.606058 0.655264 0.657909 0.575489 0.759321 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
202 NS 0.625 0.259235 0.484602 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.377814 0.451880 0.636103 0.754968 0.455257 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
203 NS 0.875 0.473073 0.515380 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.586153 0.692618 0.613392 0.568726 0.670483 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
204 NS 0.875 0.442817 0.519531 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.569525 0.673162 0.886424 0.780616 0.644456 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
205 NS 0.875 0.457466 0.462481 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.524840 0.478927 0.788753 0.819148 0.657203 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
206 NS 1.000 0.598932 0.524831 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.407963 0.636103 0.819148 0.767757 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
207 NS 0.875 0.465612 0.502613 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.569525 0.643417 0.507769 0.780616 0.664172 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
208 NS 0.625 0.245093 0.457157 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.322604 0.477464 0.726968 0.541266 0.437771 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
209 NS 0.750 0.345387 0.475778 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.448206 0.680395 0.676981 0.566208 0.551725 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
210 NS 0.500 0.161323 0.514949 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.315801 0.642330 0.633740 0.424298 0.321800 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
211 NS 0.875 0.457722 0.551976 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.608148 0.681422 0.657909 0.833639 0.657423 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
212 NS 1.000 0.602465 0.510169 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.661117 0.658458 0.673353 0.833639 0.770267 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
213 NS 0.875 0.798539 0.523123 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.769208 0.471569 0.863463 0.631465 0.894864 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
214 NS 0.750 0.335869 0.597577 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.568293 0.689583 0.657909 0.926453 0.541820 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
215 NS 0.875 0.473073 0.515380 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.586153 0.699650 0.613392 0.568726 0.670483 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
216 NS 0.625 0.241366 0.516838 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.703637 0.390853 0.501640 0.433075 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
217 NS 1.000 0.600663 0.523547 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.697648 0.788753 0.819148 0.768988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
218 NS 0.625 0.233497 0.522678 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.644503 0.507769 0.396523 0.423032 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
219 NS 1.000 0.593744 0.554506 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.689228 0.688568 0.676981 0.819148 0.764054 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
220 NS 0.875 0.457466 0.521910 0.777778 0.222222 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.581620 0.531524 0.788753 0.795109 0.657203 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
221 NS 0.625 0.287877 0.455703 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.367191 0.656330 0.983178 0.819148 0.489136 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
222 NS 0.500 0.157737 0.503437 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.293116 0.423109 0.613392 0.669127 0.316279 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
223 NS 1.000 0.964652 0.572410 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.864586 0.277350 0.863463 0.833639 0.982874 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
224 NS 0.750 0.356898 0.467234 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.448206 0.245234 0.440441 0.795109 0.563483 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
225 NS 1.000 0.600663 0.523547 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.592494 0.788753 0.833639 0.768988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
226 NS 0.750 0.349260 0.506745 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.734752 0.365397 0.450934 0.555708 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
227 NS 0.625 0.303740 0.519842 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.463590 0.473050 0.296522 0.758602 0.507083 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
228 NS 0.625 0.262405 0.492541 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.391761 0.645587 0.211247 0.819148 0.459105 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
229 NS 0.625 0.287877 0.464659 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.379618 0.437731 0.983178 0.819148 0.489136 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
230 NS 1.000 0.580887 0.509120 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.647892 0.451880 0.886424 0.833639 0.754770 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
231 NS 0.750 0.347605 0.451567 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.420618 0.668994 0.596123 0.494495 0.554010 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
232 NS 0.625 0.249320 0.502310 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.391837 0.696645 0.167409 0.693898 0.443053 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
233 NS 0.750 0.358674 0.477195 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.461426 0.507290 0.673353 0.754968 0.565276 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
234 NS 0.625 0.264484 0.499679 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.765516 0.440441 0.512729 0.461615 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
235 NS 1.000 0.595233 0.527576 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.766428 0.448680 0.795109 0.765119 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
236 NS 0.625 0.287877 0.473635 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.391761 0.242366 0.983178 0.833639 0.489136 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
237 NS 0.875 0.457639 0.492260 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.554248 0.607644 0.726968 0.764280 0.657352 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
238 NS 0.500 0.157119 0.478497 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.249590 0.493312 0.671506 0.403840 0.315323 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
239 NS 0.500 0.148902 0.551372 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.356564 0.757249 0.657909 0.833639 0.302452 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
240 NS 0.625 0.263901 0.550119 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.464405 0.428038 0.554304 0.819148 0.460912 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
241 NS 0.750 0.359389 0.516645 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.505573 0.754474 0.613392 0.575489 0.565996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
242 NS 1.000 0.583372 0.536380 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.670234 0.750757 0.726968 0.833639 0.756576 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
243 NS 0.750 0.358674 0.496286 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.483011 0.505909 0.673353 0.575489 0.565276 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
244 NS 0.875 0.475271 0.493138 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.567389 0.656330 0.569555 0.758602 0.672329 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
245 NS 0.625 0.230087 0.525209 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.403221 0.594850 0.671506 0.314638 0.418626 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
246 NS 0.750 0.361724 0.497495 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.486836 0.678336 0.569555 0.430673 0.568343 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
247 NS 0.250 0.112087 0.516613 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.257934 0.292087 0.596123 0.754968 0.241008 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
248 NS 0.500 0.148092 0.485197 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.249590 0.197891 0.665826 0.631465 0.301168 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
249 NS 0.625 0.253307 0.460545 0.888889 0.111111 0.333333 0.333333 0.235294 0.153846 0.222222 0.250 0.083333 0.250 0.260870 0.166667 0.337008 0.467099 0.596123 0.494495 0.447991 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
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14670 SC 1.000 0.606173 0.479275 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.639109 0.230536 0.245250 0.848976 0.772888 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14671 SC 0.500 0.218114 0.523104 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.388666 0.197891 0.969613 0.516019 0.402884 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14672 SC 0.250 0.114898 0.438946 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.102036 0.118262 0.586694 0.734106 0.245934 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14673 SC 0.625 0.251032 0.465477 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.341837 0.074796 0.422493 0.579083 0.445179 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14674 SC 1.000 1.000000 0.531071 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.856952 0.204837 0.841981 0.688844 1.000000 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14675 SC 0.250 0.195511 0.510431 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.347356 0.388650 0.565882 0.748002 0.371953 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14676 SC 1.000 0.607690 0.524884 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.294471 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14677 SC 0.500 0.169882 0.469573 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.250573 0.154195 0.701746 0.799844 0.334768 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14678 SC 0.500 0.218114 0.530270 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.398205 0.419790 0.969613 0.846835 0.402884 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14679 SC 0.250 0.110024 0.488802 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.203227 0.218065 0.739363 0.505118 0.237363 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14680 SC 1.000 1.000000 0.838210 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.992490 0.139930 0.841981 0.748002 1.000000 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14681 SC 1.000 0.607690 0.524884 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.334497 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14682 SC 0.375 0.172539 0.532084 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.354051 0.296836 0.969613 0.516019 0.338735 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14683 SC 0.250 0.110024 0.442564 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.102036 0.074796 0.739363 0.846835 0.237363 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14684 SC 1.000 1.000000 0.454463 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.816252 0.158666 0.841981 0.846835 1.000000 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14685 SC 0.750 0.340291 0.532056 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.506876 0.139930 0.770482 0.918792 0.546443 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14686 SC 0.750 0.340291 0.460829 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.425417 0.264443 0.770482 0.468280 0.546443 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14687 SC 0.625 0.265866 0.489132 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.390638 0.431293 0.477794 0.688844 0.463278 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14688 SC 0.750 0.388125 0.523533 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.534463 0.370220 0.440441 0.918792 0.594232 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14689 SC 1.000 0.607690 0.524884 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.456499 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14690 SC 0.250 0.089671 0.480242 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.154085 0.052889 0.701746 0.918792 0.200145 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14691 SC 0.750 0.367181 0.452264 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.439406 0.197891 0.969613 0.748002 0.573789 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14692 SC 0.750 0.361280 0.445250 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.425417 0.383214 0.732048 0.488961 0.567898 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14693 SC 0.625 0.252418 0.461868 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.338000 0.124035 0.739363 0.799844 0.446894 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14694 SC 0.625 0.293357 0.664303 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.599831 0.179354 0.969613 0.579083 0.495398 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14695 SC 0.250 0.076618 0.467357 0.444444 0.555556 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.102036 0.098945 0.378656 0.480360 0.174957 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14696 SC 0.500 0.169882 0.504419 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.309226 0.447214 0.701746 0.736267 0.334768 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14697 SC 0.500 0.223032 0.482589 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.336371 0.319528 0.456496 0.367692 0.409402 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14698 SC 0.500 0.223032 0.479395 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.331561 0.112194 0.456496 0.409505 0.409402 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14699 SC 0.500 0.218114 0.527482 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.394515 0.091606 0.969613 0.918792 0.402884 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14700 SC 0.125 0.096677 0.495322 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.196494 0.296836 0.739363 0.680480 0.213226 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14701 SC 1.000 1.000000 0.401336 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.785866 0.436130 0.841981 0.680480 1.000000 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14702 SC 0.250 0.110024 0.530831 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.279794 0.303822 0.739363 0.725219 0.237363 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14703 SC 0.500 0.165079 0.535573 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.351084 0.144841 0.655863 0.616807 0.327526 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14704 SC 0.000 0.033730 0.499696 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.103271 0.083624 0.739363 0.539099 0.083560 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14705 SC 0.625 0.254528 0.482818 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.370501 0.498950 0.586694 0.734106 0.449495 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14706 SC 1.000 0.607690 0.524884 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.105777 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14707 SC 0.500 0.164645 0.467609 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.221249 0.580068 0.846835 0.326867 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14708 SC 0.625 0.261481 0.472820 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.363702 0.214834 0.628902 0.516019 0.457986 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14709 SC 0.250 0.076618 0.467357 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.102036 0.303822 0.378656 0.505118 0.174957 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14710 SC 0.625 0.259914 0.420545 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.091606 0.402145 0.505978 0.456083 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14711 SC 0.375 0.172539 0.535106 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.358397 0.323875 0.969613 0.758602 0.338735 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14712 SC 0.750 0.342349 0.444022 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.405996 0.445647 0.655863 0.516019 0.548582 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14713 SC 0.625 0.238561 0.515574 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.398837 0.402787 0.541772 0.616807 0.429515 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14714 SC 0.750 0.344670 0.468971 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.439406 0.214834 0.621344 0.736267 0.550985 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14715 SC 0.750 0.463177 0.491234 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.557181 0.236525 0.616089 0.360673 0.662097 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14716 SC 0.750 0.352930 0.508416 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.491632 0.582975 0.513116 0.535434 0.559457 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14717 SC 0.250 0.132393 0.511317 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.274864 0.154195 0.969613 0.725219 0.275697 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14718 SC 0.500 0.160065 0.447026 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.037398 0.717649 0.688844 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14719 SC 0.875 0.508364 0.520069 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.612970 0.379547 0.841981 0.799844 0.699439 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14720 SC 1.000 1.000000 0.550712 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.866857 0.230536 0.841981 0.409505 1.000000 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14721 SC 0.750 0.463177 0.465749 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.532391 0.245234 0.616089 0.799844 0.662097 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14722 SC 0.500 0.158933 0.519815 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.516849 0.365397 0.846835 0.318127 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14723 SC 0.875 0.837109 0.579979 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.818806 0.245234 0.841981 0.367692 0.916518 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14724 SC 0.625 0.234250 0.439593 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.052889 0.717649 0.846835 0.424001 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14725 SC 0.625 0.293357 0.481981 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.408063 0.218065 0.969613 0.846835 0.495398 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14726 SC 0.625 0.234250 0.499706 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.373061 0.272261 0.717649 0.735729 0.424001 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14727 SC 0.250 0.114898 0.498396 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.228628 0.518200 0.586694 0.505118 0.245934 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14728 SC 0.625 0.293357 0.469208 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.391279 0.428038 0.969613 0.409505 0.495398 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14729 SC 0.625 0.235386 0.468240 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.328546 0.284814 0.849676 0.758602 0.425458 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14730 SC 1.000 0.586702 0.509296 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.651409 0.477464 0.770482 0.918792 0.758987 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14731 SC 0.250 0.114898 0.465368 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.162427 0.074796 0.586694 0.688844 0.245934 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14732 SC 0.500 0.170375 0.487136 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281639 0.306115 0.334818 0.736267 0.335505 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14733 SC 0.625 0.256791 0.499538 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.395526 0.000000 0.583417 0.735729 0.452272 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14734 SC 0.500 0.160065 0.463095 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.225288 0.052889 0.717649 0.758602 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14735 SC 0.875 0.837109 0.451333 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.742084 0.230536 0.841981 0.725219 0.916518 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14736 SC 1.000 0.607690 0.524884 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.540655 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14737 SC 0.750 0.367181 0.475009 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.466176 0.323875 0.969613 0.799844 0.573789 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14738 SC 0.500 0.218114 0.513914 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.376150 0.274817 0.969613 0.758602 0.402884 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14739 SC 0.250 0.132393 0.541011 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.322926 0.413073 0.969613 0.758602 0.275697 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14740 SC 0.375 0.172539 0.516596 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.331146 0.284814 0.969613 0.846835 0.338735 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14741 SC 0.875 0.464316 0.555215 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.615037 0.362586 0.841373 0.918792 0.663069 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14742 SC 0.625 0.268222 0.488330 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.391903 0.239463 0.211247 0.735729 0.466100 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14743 SC 0.500 0.160065 0.576298 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.402071 0.323875 0.717649 0.918792 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14744 SC 0.500 0.218114 0.459903 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.295205 0.052889 0.969613 0.579083 0.402884 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14745 SC 0.875 0.461612 0.452213 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.517493 0.340711 0.626424 0.861934 0.660760 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14746 SC 0.375 0.172539 0.467151 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.249766 0.358708 0.969613 0.748002 0.338735 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14747 SC 0.750 0.340291 0.449436 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.411010 0.204837 0.770482 0.531701 0.546443 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14748 SC 0.625 0.235386 0.550049 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.439366 0.467099 0.849676 0.758602 0.425458 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14749 SC 1.000 0.607690 0.524884 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.296836 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14750 SC 0.625 0.259849 0.506851 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.408063 0.064775 0.513116 0.688844 0.456004 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14751 SC 1.000 0.607690 0.524884 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.299183 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14752 SC 0.875 0.512898 0.519157 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.615037 0.494727 0.105623 0.918792 0.703057 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14753 SC 0.625 0.234250 0.541092 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.427357 0.332400 0.717649 0.734106 0.424001 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14754 SC 0.500 0.218114 0.523038 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.388577 0.423109 0.969613 0.758602 0.402884 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14755 SC 0.750 0.349430 0.550172 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.532391 0.284814 0.739363 0.799844 0.555882 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14756 SC 0.750 0.356661 0.482862 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.466227 0.544521 0.334818 0.736267 0.563243 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14757 SC 1.000 1.000000 0.000000 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.460032 0.052889 0.841981 0.680480 1.000000 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14758 SC 0.500 0.169882 0.521766 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.335966 0.564696 0.701746 0.799844 0.334768 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14759 SC 0.500 0.160065 0.478768 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.253976 0.098945 0.717649 0.645114 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14760 SC 0.500 0.218114 0.427924 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.256387 0.969613 0.736267 0.402884 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14761 SC 0.500 0.160065 0.505747 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.299688 0.328165 0.717649 0.758602 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14762 SC 0.750 0.367181 0.406693 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.380840 0.250873 0.969613 0.748002 0.573789 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14763 SC 0.625 0.293357 0.553307 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.492365 0.454965 0.969613 0.918792 0.495398 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14764 SC 0.500 0.160065 0.468280 0.555556 0.444444 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.234968 0.052889 0.717649 0.516019 0.319870 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14765 SC 0.875 0.478059 0.542561 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.612970 0.301511 0.739363 0.799844 0.674663 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14766 SC 0.750 0.367181 0.440871 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.425417 0.118262 0.969613 0.748002 0.573789 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14767 SC 0.500 0.169882 0.521766 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.335966 0.453425 0.701746 0.799844 0.334768 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14768 SC 0.875 0.464316 0.429341 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.495521 0.134840 0.841373 0.748002 0.663069 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14769 SC 0.875 0.837109 0.415504 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.718350 0.294471 0.841981 0.799844 0.916518 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0
14770 SC 0.500 0.218114 0.427924 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.289683 0.969613 0.480360 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14771 SC 0.500 0.160065 0.502987 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.295205 0.385035 0.717649 0.748002 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14772 SC 0.250 0.090713 0.470107 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.133175 0.261785 0.350873 0.848976 0.202108 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14773 SC 0.500 0.161098 0.470241 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.366423 0.334818 0.736267 0.321454 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14774 SC 0.000 0.159537 0.489906 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.272705 0.064775 0.565882 0.848976 0.319056 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14775 SC 0.125 0.096677 0.475599 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.155301 0.330289 0.739363 0.725219 0.213226 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14776 SC 0.750 0.353487 0.416856 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.380840 0.118262 0.316870 0.748002 0.560024 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14777 SC 0.750 0.367181 0.395300 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.365016 0.211554 0.969613 0.579083 0.573789 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14778 SC 0.250 0.114898 0.503089 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.237339 0.064775 0.586694 0.748002 0.245934 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14779 SC 0.375 0.172539 0.459874 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.236540 0.118262 0.969613 0.579083 0.338735 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14780 SC 0.250 0.088099 0.477117 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.144611 0.000000 0.167409 0.673230 0.197168 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14781 SC 1.000 0.607690 0.524884 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.456499 0.841373 0.861934 0.773957 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14782 SC 0.500 0.218114 0.440873 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.263110 0.245234 0.969613 0.667233 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14783 SC 0.500 0.218114 0.486239 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.336371 0.319528 0.969613 0.725219 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14784 SC 0.500 0.169882 0.511688 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.296836 0.701746 0.686786 0.334768 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14785 SC 0.625 0.234250 0.511132 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.388671 0.245234 0.717649 0.918792 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14786 SC 0.625 0.234250 0.537923 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.423408 0.600699 0.717649 0.848976 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14787 SC 0.750 0.367181 0.454123 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.441650 0.134840 0.969613 0.846835 0.573789 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14788 SC 0.625 0.293357 0.471932 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.394907 0.454965 0.969613 0.686786 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14789 SC 0.500 0.218114 0.444223 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.268917 0.124035 0.969613 0.680480 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14790 SC 0.625 0.579270 0.448921 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.597065 0.083624 0.422493 0.725219 0.753592 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14791 SC 1.000 0.607690 0.550171 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.693547 0.346814 0.841373 0.848976 0.773957 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14792 SC 0.500 0.218114 0.451909 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281983 0.074796 0.969613 0.531701 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14793 SC 0.750 0.322942 0.473705 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.425417 0.368326 0.717649 0.619246 0.528092 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14794 SC 0.250 0.072108 0.538431 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.243886 0.296836 0.490500 0.918792 0.165993 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14795 SC 0.875 0.484634 0.508380 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.587563 0.612230 0.621344 0.799844 0.680129 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14796 SC 0.500 0.169882 0.487707 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281983 0.640149 0.701746 0.848976 0.334768 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14797 SC 0.750 0.388125 0.511256 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.522104 0.630240 0.440441 0.616807 0.594232 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14798 SC 0.750 0.351459 0.441147 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.411010 0.214834 0.211247 0.505118 0.557957 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14799 SC 0.625 0.261481 0.507752 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.410784 0.650981 0.628902 0.618761 0.457986 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14800 SC 0.500 0.218114 0.483045 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.331561 0.236525 0.969613 0.846835 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14801 SC 0.875 0.447064 0.478900 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.533579 0.505909 0.770482 0.736267 0.648180 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14802 SC 0.750 0.349430 0.510850 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.491454 0.340711 0.739363 0.918792 0.555882 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14803 SC 0.625 0.235386 0.525642 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.408882 0.264443 0.849676 0.918792 0.425458 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14804 SC 0.750 0.361280 0.433857 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.411010 0.392232 0.732048 0.736267 0.567898 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14805 SC 1.000 0.641071 0.467879 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.650577 0.037398 0.841981 0.680480 0.797000 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14806 SC 0.625 0.261973 0.447769 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.327422 0.118262 0.456496 0.688844 0.458582 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14807 SC 0.875 0.837109 0.396879 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.705536 0.272261 0.841981 0.531701 0.916518 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14808 SC 0.625 0.254056 0.535152 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.438105 0.498950 0.532806 0.673230 0.448914 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14809 SC 0.625 0.234250 0.497098 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.369425 0.484732 0.717649 0.725219 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14810 SC 0.375 0.155274 0.505715 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.293854 0.373979 0.586694 0.735729 0.312458 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14811 SC 0.500 0.222438 0.491851 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.349409 0.204837 0.565882 0.848976 0.408618 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14812 SC 1.000 0.613538 0.520543 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.416445 0.739363 0.799844 0.778061 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14813 SC 0.250 0.132393 0.505211 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.264358 0.264443 0.969613 0.516019 0.275697 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14814 SC 0.625 0.293357 0.414894 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.312570 0.171379 0.969613 0.619246 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14815 SC 0.750 0.328868 0.565433 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.532391 0.261785 0.849676 0.861934 0.534426 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14816 SC 0.500 0.208952 0.482199 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.319850 0.315120 0.541772 0.734106 0.390543 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14817 SC 0.250 0.087269 0.470198 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.127515 0.424758 0.607850 0.686786 0.195590 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14818 SC 0.625 0.244639 0.490132 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.370467 0.250873 0.713474 0.846835 0.437202 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14819 SC 0.125 0.096677 0.463130 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.127316 0.154195 0.739363 0.736267 0.213226 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14820 SC 0.625 0.293357 0.505524 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.437548 0.310650 0.969613 0.667233 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14821 SC 0.625 0.234250 0.487515 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.355828 0.414763 0.717649 0.671002 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14822 SC 0.250 0.077135 0.484214 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.142287 0.112194 0.713474 0.595044 0.175975 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14823 SC 0.625 0.244011 0.497110 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.379472 0.308391 0.456496 0.918792 0.436412 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14824 SC 1.000 0.616228 0.471812 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.639109 0.259100 0.732048 0.848976 0.779939 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14825 SC 0.500 0.160065 0.518974 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.264443 0.717649 0.360673 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14826 SC 0.250 0.195511 0.493535 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.321953 0.083624 0.565882 0.748002 0.371953 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14827 SC 0.500 0.165079 0.483274 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.268380 0.362586 0.655863 0.846835 0.327526 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14828 SC 0.500 0.172394 0.539702 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.364779 0.395782 0.350873 0.645114 0.338519 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14829 SC 0.250 0.089671 0.475135 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.142791 0.284814 0.701746 0.799844 0.200145 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14830 SC 0.625 0.251256 0.446141 0.666667 0.333333 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.312570 0.319528 0.701746 0.848976 0.445456 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14831 SC 0.750 0.340291 0.495011 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.466227 0.112194 0.770482 0.734106 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14832 SC 0.500 0.132550 0.467448 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.000000 0.402145 0.618761 0.275957 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14833 SC 0.625 0.293357 0.446030 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.359246 0.105777 0.969613 0.848976 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14834 SC 0.875 0.466099 0.434230 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.502169 0.144841 0.655863 0.861934 0.664586 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14835 SC 0.500 0.160065 0.455020 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.209811 0.158666 0.717649 0.579083 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14836 SC 1.000 1.000000 0.350351 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.754788 0.267074 0.841981 0.616807 1.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14837 SC 0.250 0.093623 0.461342 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.117857 0.264443 0.655863 0.848976 0.207560 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14838 SC 0.250 0.195511 0.493535 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.321953 0.158666 0.565882 0.848976 0.371953 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14839 SC 1.000 0.608972 0.523932 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.675124 0.292087 0.105623 0.861934 0.774860 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14840 SC 0.875 0.837109 0.515709 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.782000 0.214834 0.841981 0.799844 0.916518 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0
14841 SC 0.375 0.172539 0.478363 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.269460 0.390445 0.969613 0.725219 0.338735 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14842 SC 0.625 0.293357 0.395724 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.186989 0.969613 0.531701 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14843 SC 0.625 0.254528 0.482048 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.369425 0.350823 0.586694 0.846835 0.449495 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14844 SC 0.250 0.114898 0.454864 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.139315 0.397545 0.586694 0.645114 0.245934 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14845 SC 0.750 0.371738 0.533615 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.532391 0.312893 0.732048 0.799844 0.578299 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14846 SC 0.625 0.225503 0.489048 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.348638 0.377700 0.841373 0.688844 0.412649 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14847 SC 0.750 0.331997 0.489774 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.453005 0.386847 0.365397 0.480360 0.537742 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14848 SC 0.500 0.218114 0.505495 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.364391 0.118262 0.969613 0.725219 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14849 SC 0.625 0.244766 0.441375 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.297246 0.163014 0.655863 0.748002 0.437361 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14850 SC 0.500 0.142165 0.460311 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.124035 0.593044 0.505118 0.291682 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14851 SC 0.625 0.234250 0.458763 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.312570 0.134840 0.717649 0.736267 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14852 SC 0.625 0.240432 0.463757 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.327422 0.471569 0.273032 0.619246 0.431892 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14853 SC 0.875 0.512898 0.519157 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.615037 0.372104 0.105623 0.918792 0.703057 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14854 SC 0.625 0.428333 0.436190 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.473638 0.064775 0.616089 0.848976 0.631572 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14855 SC 0.500 0.222438 0.491851 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.349409 0.129550 0.565882 0.848976 0.408618 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14856 SC 0.375 0.172539 0.438051 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.194555 0.274817 0.969613 0.556471 0.338735 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14857 SC 0.750 0.338935 0.444992 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.403963 0.330289 0.541772 0.586081 0.545029 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14858 SC 0.250 0.132393 0.447666 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.152183 0.124035 0.969613 0.846835 0.275697 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14859 SC 0.875 0.499183 0.551581 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.633393 0.609941 0.565882 0.846835 0.692044 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14860 SC 0.500 0.186544 0.509400 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.335966 0.578157 0.586694 0.799844 0.359210 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14861 SC 0.750 0.365018 0.463484 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.450905 0.274817 0.583417 0.686786 0.571637 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14862 SC 0.375 0.172539 0.509180 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.319780 0.303822 0.969613 0.846835 0.338735 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14863 SC 0.500 0.132550 0.467448 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.000000 0.402145 0.686786 0.275957 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14864 SC 0.500 0.135963 0.488896 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.098945 0.541772 0.480360 0.281589 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14865 SC 0.625 0.234250 0.439593 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.064775 0.717649 0.579083 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14866 SC 0.750 0.360261 0.484125 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.470712 0.296836 0.402145 0.758602 0.566874 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14867 SC 0.750 0.340291 0.517797 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.491632 0.330289 0.770482 0.667233 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14868 SC 0.750 0.351243 0.517731 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.500311 0.599534 0.378656 0.688844 0.557737 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14869 SC 0.250 0.132393 0.522800 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.294021 0.491892 0.969613 0.725219 0.275697 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14870 SC 0.750 0.340291 0.517797 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.491632 0.259100 0.770482 0.468280 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14871 SC 0.500 0.218114 0.411936 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.209811 0.118262 0.969613 0.736267 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14872 SC 0.750 0.367804 0.427594 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.409183 0.245234 0.532806 0.645114 0.574408 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14873 SC 0.875 0.480062 0.511774 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.587563 0.377700 0.477794 0.799844 0.676333 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14874 SC 1.000 0.615174 0.455358 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.625116 0.118262 0.541772 0.848976 0.779204 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14875 SC 0.500 0.170669 0.493179 0.777778 0.222222 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.292034 0.230536 0.739363 0.918792 0.335946 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14876 SC 0.500 0.172394 0.453863 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.225133 0.319528 0.350873 0.680480 0.338519 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14877 SC 0.500 0.165079 0.491804 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.282875 0.171379 0.655863 0.671002 0.327526 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14878 SC 0.750 0.340291 0.513956 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.487442 0.118262 0.770482 0.688844 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14879 SC 0.875 0.837109 0.507834 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.777291 0.112194 0.841981 0.846835 0.916518 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14880 SC 0.875 0.535390 0.500011 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.612970 0.183211 0.616089 0.799844 0.720679 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14881 SC 0.500 0.160065 0.528979 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.335855 0.074796 0.717649 0.918792 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14882 SC 0.500 0.223032 0.587630 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.472585 0.208223 0.456496 0.918792 0.409402 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14883 SC 0.375 0.172539 0.516711 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.331319 0.083624 0.969613 0.846835 0.338735 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14884 SC 0.500 0.148496 0.495581 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.268380 0.299183 0.770482 0.846835 0.301809 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14885 SC 0.875 0.473794 0.391740 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.461412 0.129550 0.316870 0.735729 0.671089 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14886 SC 0.625 0.256791 0.422863 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.112194 0.583417 0.748002 0.452272 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14887 SC 0.250 0.089671 0.464275 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.117857 0.714485 0.701746 0.579083 0.200145 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14888 SC 0.500 0.164645 0.467609 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.224387 0.580068 0.846835 0.326867 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14889 SC 0.625 0.428333 0.486561 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.527325 0.368326 0.616089 0.748002 0.631572 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14890 SC 1.000 0.589827 0.498553 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.644793 0.279860 0.365397 0.481335 0.761241 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14891 SC 0.750 0.340291 0.480293 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.449077 0.272261 0.770482 0.848976 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14892 SC 1.000 0.608502 0.512015 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.665929 0.406245 0.732048 0.848976 0.774529 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14893 SC 0.625 0.225503 0.532342 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.408063 0.390445 0.841373 0.680480 0.412649 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14894 SC 0.500 0.160065 0.447026 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.037398 0.717649 0.319093 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14895 SC 0.750 0.340291 0.460829 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.425417 0.248069 0.770482 0.671002 0.546443 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14896 SC 0.500 0.165079 0.467286 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.296836 0.655863 0.260582 0.327526 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14897 SC 0.500 0.152474 0.457030 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.202701 0.105777 0.626424 0.667233 0.308082 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14898 SC 0.500 0.218114 0.495265 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.349708 0.419790 0.969613 0.846835 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14899 SC 0.500 0.167121 0.513737 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.326027 0.565882 0.618761 0.330616 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14900 SC 0.625 0.252418 0.545295 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.448819 0.124035 0.739363 0.799844 0.446894 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14901 SC 0.500 0.160065 0.471007 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.239984 0.279860 0.717649 0.846835 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14902 SC 0.500 0.170669 0.511104 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.658458 0.739363 0.531701 0.335946 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14903 SC 0.250 0.132393 0.432567 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.117857 0.124035 0.969613 0.680480 0.275697 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14904 SC 0.375 0.155274 0.443591 0.888889 0.111111 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.179710 0.105777 0.586694 0.725219 0.312458 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14905 SC 0.000 0.033153 0.512042 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.131377 0.323875 0.167409 0.918792 0.082226 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14906 SC 0.625 0.234250 0.525850 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.408063 0.064775 0.717649 0.300637 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14907 SC 0.750 0.331715 0.524162 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.491632 0.563456 0.841373 0.846835 0.537444 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14908 SC 0.500 0.132550 0.467448 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.193987 0.000000 0.402145 0.505118 0.275957 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
14909 SC 0.625 0.234250 0.439593 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.281412 0.253645 0.717649 0.480360 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14910 SC 1.000 1.000000 0.372559 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.768572 0.287259 0.841981 0.918792 1.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0
14911 SC 0.500 0.160065 0.455020 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.209811 0.201394 0.717649 0.918792 0.319870 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0
14912 SC 0.625 0.293357 0.514911 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.448819 0.399300 0.969613 0.799844 0.495398 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14913 SC 0.500 0.543706 0.659599 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.735906 0.037398 0.422493 0.645114 0.727061 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0
14914 SC 0.500 0.223032 0.472241 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.320611 0.214834 0.456496 0.736267 0.409402 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14915 SC 0.500 0.218114 0.570755 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.448819 0.230536 0.969613 0.848976 0.402884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14916 SC 0.875 0.464316 0.498718 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.565015 0.570854 0.841373 0.667233 0.663069 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14917 SC 0.625 0.234250 0.468345 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.327422 0.204837 0.717649 0.736267 0.424001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14918 SC 0.750 0.355550 0.545630 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.532391 0.245234 0.273032 0.799844 0.562118 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
14919 SC 0.250 0.067271 0.480900 1.000000 0.000000 0.333333 0.666667 0.352941 0.000000 0.444444 0.625 0.000000 0.125 0.217391 0.333333 0.117857 0.134840 0.717649 0.680480 0.156225 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0

14920 rows × 98 columns

Perform logistic regression with intent to extract only most important features

In [132]:
%%R

vars <- unlist(fullColumns)
vars <- vars[-c(94,95)]
#print(vars)
fla <- paste("SEP ~", paste(vars, collapse="+"))
fla <- as.formula(fla)

OPMAnalysisDataNoFamBinary$SEP <- as.vector(y)
BinLogit <- glm(fla, data = OPMAnalysisDataNoFamBinary, family = "binomial")
summary(BinLogit)
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0182  -0.6661  -0.1038   0.7300   3.3486  

Coefficients: (8 not defined because of singularities)
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    -4.197348   1.576487  -2.662 0.007757 ** 
GSEGRD                         -4.648744   0.378877 -12.270  < 2e-16 ***
IndAvgSalary                   -2.226607   0.846708  -2.630 0.008545 ** 
SalaryOverUnderIndAvg          -0.439162   1.619677  -0.271 0.786282    
LowerLimitAge                  -2.540729   1.212200  -2.096 0.036085 *  
YearsToRetirement                     NA         NA      NA       NA    
BLS_FEDERAL_OtherSep_Rate      17.409681   1.876284   9.279  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         32.683546   2.923824  11.178  < 2e-16 ***
BLS_FEDERAL_TotalSep_Level    -11.140742   9.295826  -1.198 0.230735    
BLS_FEDERAL_JobOpenings_Rate  -28.952564   7.797843  -3.713 0.000205 ***
BLS_FEDERAL_OtherSep_Level    -13.160928   1.193940 -11.023  < 2e-16 ***
BLS_FEDERAL_Quits_Level       -16.573381   2.709025  -6.118 9.49e-10 ***
BLS_FEDERAL_JobOpenings_Level  26.061857   7.228661   3.605 0.000312 ***
BLS_FEDERAL_Layoffs_Rate      -48.541013   3.573051 -13.585  < 2e-16 ***
BLS_FEDERAL_Layoffs_Level      91.075716   9.095455  10.013  < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate     -37.146001   3.619372 -10.263  < 2e-16 ***
SALARYLog                       0.778065   1.458424   0.533 0.593689    
LOSSqrt                        -6.546830   0.205469 -31.863  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.385652   0.111369  -3.463 0.000534 ***
SEPCount_EFDATE_LOCLog         -2.285935   0.658082  -3.474 0.000513 ***
IndAvgSalaryLog                 7.153809   1.471637   4.861 1.17e-06 ***
AGELVL_B                       -0.986586   1.153840  -0.855 0.392526    
AGELVL_C                       -0.374074   0.999550  -0.374 0.708224    
AGELVL_D                       -0.153536   0.864342  -0.178 0.859011    
AGELVL_E                        0.169895   0.730631   0.233 0.816125    
AGELVL_F                        0.425987   0.597625   0.713 0.475969    
AGELVL_G                        0.719669   0.465324   1.547 0.121961    
AGELVL_H                        0.878826   0.334395   2.628 0.008586 ** 
AGELVL_I                        0.751104   0.208116   3.609 0.000307 ***
AGELVL_J                              NA         NA      NA       NA    
AGELVL_K                              NA         NA      NA       NA    
LOC_01                          0.046480   0.414445   0.112 0.910704    
LOC_02                          0.170594   0.449780   0.379 0.704477    
LOC_04                          0.884459   0.419602   2.108 0.035044 *  
LOC_05                          0.127465   0.434442   0.293 0.769216    
LOC_06                          1.441664   0.498656   2.891 0.003839 ** 
LOC_08                          0.619457   0.424039   1.461 0.144057    
LOC_09                         -0.479861   0.498851  -0.962 0.336083    
LOC_10                         -0.571930   0.719466  -0.795 0.426650    
LOC_11                          0.854293   0.482586   1.770 0.076687 .  
LOC_12                          0.730602   0.437520   1.670 0.094945 .  
LOC_13                          0.767622   0.428145   1.793 0.072989 .  
LOC_15                         -0.012564   0.405718  -0.031 0.975296    
LOC_16                          0.104116   0.457768   0.227 0.820080    
LOC_17                          0.222340   0.418562   0.531 0.595280    
LOC_18                         -0.040707   0.445074  -0.091 0.927127    
LOC_19                          0.002682   0.494368   0.005 0.995671    
LOC_20                          0.713290   0.450296   1.584 0.113183    
LOC_21                          0.121747   0.424599   0.287 0.774316    
LOC_22                         -0.061715   0.421059  -0.147 0.883470    
LOC_23                          0.032093   0.505883   0.063 0.949416    
LOC_24                          0.703076   0.456910   1.539 0.123862    
LOC_25                          0.330490   0.419391   0.788 0.430683    
LOC_26                          0.160693   0.422700   0.380 0.703828    
LOC_27                          0.517693   0.443432   1.167 0.243021    
LOC_28                          0.109315   0.441527   0.248 0.804456    
LOC_29                          0.200044   0.436890   0.458 0.647037    
LOC_30                          0.714635   0.440294   1.623 0.104571    
LOC_31                         -0.100029   0.496424  -0.201 0.840309    
LOC_32                          0.266571   0.446144   0.598 0.550173    
LOC_33                         -0.268449   0.648422  -0.414 0.678872    
LOC_34                          0.001309   0.419181   0.003 0.997509    
LOC_35                          0.747138   0.407297   1.834 0.066598 .  
LOC_36                          0.562931   0.438910   1.283 0.199644    
LOC_37                          0.540114   0.422464   1.278 0.201079    
LOC_38                          0.316471   0.520899   0.608 0.543487    
LOC_39                          0.274637   0.418926   0.656 0.512098    
LOC_40                          0.449613   0.413569   1.087 0.276969    
LOC_41                          0.472063   0.430142   1.097 0.272440    
LOC_42                          0.241938   0.434815   0.556 0.577926    
LOC_44                         -0.354656   0.667342  -0.531 0.595110    
LOC_45                          0.198677   0.430252   0.462 0.644247    
LOC_46                          0.541219   0.446046   1.213 0.224988    
LOC_47                          0.048904   0.435216   0.112 0.910533    
LOC_48                          1.274278   0.477731   2.667 0.007645 ** 
LOC_49                          0.233045   0.433959   0.537 0.591254    
LOC_50                         -0.465553   0.711575  -0.654 0.512947    
LOC_51                          0.619192   0.471212   1.314 0.188832    
LOC_53                          0.941819   0.432155   2.179 0.029305 *  
LOC_54                         -0.098544   0.454643  -0.217 0.828404    
LOC_55                         -0.258949   0.446015  -0.581 0.561521    
LOC_56                                NA         NA      NA       NA    
TOA_10                         -1.147810   0.208103  -5.516 3.48e-08 ***
TOA_15                         -1.078670   0.213121  -5.061 4.16e-07 ***
TOA_20                          0.015746   0.280101   0.056 0.955171    
TOA_30                         -0.713454   0.223705  -3.189 0.001426 ** 
TOA_32                         -0.349741   0.799848  -0.437 0.661924    
TOA_35                         -1.502739   0.361130  -4.161 3.17e-05 ***
TOA_38                         -0.746850   0.213613  -3.496 0.000472 ***
TOA_40                         -1.049221   0.270570  -3.878 0.000105 ***
TOA_42                          0.064794   0.489778   0.132 0.894753    
TOA_44                          1.317649   1.130979   1.165 0.243998    
TOA_45                          8.104085 139.230042   0.058 0.953584    
TOA_48                                NA         NA      NA       NA    
PPGROUP_11                      0.379866   0.158079   2.403 0.016261 *  
PPGROUP_12                            NA         NA      NA       NA    
TOATYP_1                              NA         NA      NA       NA    
TOATYP_2                              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13443  on 14830  degrees of freedom
AIC: 13623

Number of Fisher Scoring iterations: 10

In [133]:
%%R
alias(BinLogit)
Model :
SEP ~ GSEGRD + IndAvgSalary + SalaryOverUnderIndAvg + LowerLimitAge + 
    YearsToRetirement + BLS_FEDERAL_OtherSep_Rate + BLS_FEDERAL_Quits_Rate + 
    BLS_FEDERAL_TotalSep_Level + BLS_FEDERAL_JobOpenings_Rate + 
    BLS_FEDERAL_OtherSep_Level + BLS_FEDERAL_Quits_Level + BLS_FEDERAL_JobOpenings_Level + 
    BLS_FEDERAL_Layoffs_Rate + BLS_FEDERAL_Layoffs_Level + BLS_FEDERAL_TotalSep_Rate + 
    SALARYLog + LOSSqrt + SEPCount_EFDATE_OCCLog + SEPCount_EFDATE_LOCLog + 
    IndAvgSalaryLog + AGELVL_B + AGELVL_C + AGELVL_D + AGELVL_E + 
    AGELVL_F + AGELVL_G + AGELVL_H + AGELVL_I + AGELVL_J + AGELVL_K + 
    LOC_01 + LOC_02 + LOC_04 + LOC_05 + LOC_06 + LOC_08 + LOC_09 + 
    LOC_10 + LOC_11 + LOC_12 + LOC_13 + LOC_15 + LOC_16 + LOC_17 + 
    LOC_18 + LOC_19 + LOC_20 + LOC_21 + LOC_22 + LOC_23 + LOC_24 + 
    LOC_25 + LOC_26 + LOC_27 + LOC_28 + LOC_29 + LOC_30 + LOC_31 + 
    LOC_32 + LOC_33 + LOC_34 + LOC_35 + LOC_36 + LOC_37 + LOC_38 + 
    LOC_39 + LOC_40 + LOC_41 + LOC_42 + LOC_44 + LOC_45 + LOC_46 + 
    LOC_47 + LOC_48 + LOC_49 + LOC_50 + LOC_51 + LOC_53 + LOC_54 + 
    LOC_55 + LOC_56 + TOA_10 + TOA_15 + TOA_20 + TOA_30 + TOA_32 + 
    TOA_35 + TOA_38 + TOA_40 + TOA_42 + TOA_44 + TOA_45 + TOA_48 + 
    PPGROUP_11 + PPGROUP_12 + TOATYP_1 + TOATYP_2

Complete :
                  (Intercept) GSEGRD IndAvgSalary SalaryOverUnderIndAvg
YearsToRetirement  1           0      0            0                   
AGELVL_J           9           0      0            0                   
AGELVL_K          -8           0      0            0                   
LOC_56             1           0      0            0                   
TOA_48             1           0      0            0                   
PPGROUP_12         1           0      0            0                   
TOATYP_1           0           0      0            0                   
TOATYP_2           1           0      0            0                   
                  LowerLimitAge BLS_FEDERAL_OtherSep_Rate
YearsToRetirement -1             0                       
AGELVL_J          -9             0                       
AGELVL_K           9             0                       
LOC_56             0             0                       
TOA_48             0             0                       
PPGROUP_12         0             0                       
TOATYP_1           0             0                       
TOATYP_2           0             0                       
                  BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level
YearsToRetirement  0                      0                        
AGELVL_J           0                      0                        
AGELVL_K           0                      0                        
LOC_56             0                      0                        
TOA_48             0                      0                        
PPGROUP_12         0                      0                        
TOATYP_1           0                      0                        
TOATYP_2           0                      0                        
                  BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level
YearsToRetirement  0                            0                        
AGELVL_J           0                            0                        
AGELVL_K           0                            0                        
LOC_56             0                            0                        
TOA_48             0                            0                        
PPGROUP_12         0                            0                        
TOATYP_1           0                            0                        
TOATYP_2           0                            0                        
                  BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level
YearsToRetirement  0                       0                           
AGELVL_J           0                       0                           
AGELVL_K           0                       0                           
LOC_56             0                       0                           
TOA_48             0                       0                           
PPGROUP_12         0                       0                           
TOATYP_1           0                       0                           
TOATYP_2           0                       0                           
                  BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level
YearsToRetirement  0                        0                       
AGELVL_J           0                        0                       
AGELVL_K           0                        0                       
LOC_56             0                        0                       
TOA_48             0                        0                       
PPGROUP_12         0                        0                       
TOATYP_1           0                        0                       
TOATYP_2           0                        0                       
                  BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt
YearsToRetirement  0                         0         0     
AGELVL_J           0                         0         0     
AGELVL_K           0                         0         0     
LOC_56             0                         0         0     
TOA_48             0                         0         0     
PPGROUP_12         0                         0         0     
TOATYP_1           0                         0         0     
TOATYP_2           0                         0         0     
                  SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
YearsToRetirement  0                      0                      0             
AGELVL_J           0                      0                      0             
AGELVL_K           0                      0                      0             
LOC_56             0                      0                      0             
TOA_48             0                      0                      0             
PPGROUP_12         0                      0                      0             
TOATYP_1           0                      0                      0             
TOATYP_2           0                      0                      0             
                  AGELVL_B AGELVL_C AGELVL_D AGELVL_E AGELVL_F AGELVL_G
YearsToRetirement  0        0        0        0        0        0      
AGELVL_J          -9       -8       -7       -6       -5       -4      
AGELVL_K           8        7        6        5        4        3      
LOC_56             0        0        0        0        0        0      
TOA_48             0        0        0        0        0        0      
PPGROUP_12         0        0        0        0        0        0      
TOATYP_1           0        0        0        0        0        0      
TOATYP_2           0        0        0        0        0        0      
                  AGELVL_H AGELVL_I LOC_01 LOC_02 LOC_04 LOC_05 LOC_06 LOC_08
YearsToRetirement  0        0        0      0      0      0      0      0    
AGELVL_J          -3       -2        0      0      0      0      0      0    
AGELVL_K           2        1        0      0      0      0      0      0    
LOC_56             0        0       -1     -1     -1     -1     -1     -1    
TOA_48             0        0        0      0      0      0      0      0    
PPGROUP_12         0        0        0      0      0      0      0      0    
TOATYP_1           0        0        0      0      0      0      0      0    
TOATYP_2           0        0        0      0      0      0      0      0    
                  LOC_09 LOC_10 LOC_11 LOC_12 LOC_13 LOC_15 LOC_16 LOC_17
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1     -1     -1     -1     -1    
TOA_48             0      0      0      0      0      0      0      0    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      0      0      0      0    
TOATYP_2           0      0      0      0      0      0      0      0    
                  LOC_18 LOC_19 LOC_20 LOC_21 LOC_22 LOC_23 LOC_24 LOC_25
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1     -1     -1     -1     -1    
TOA_48             0      0      0      0      0      0      0      0    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      0      0      0      0    
TOATYP_2           0      0      0      0      0      0      0      0    
                  LOC_26 LOC_27 LOC_28 LOC_29 LOC_30 LOC_31 LOC_32 LOC_33
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1     -1     -1     -1     -1    
TOA_48             0      0      0      0      0      0      0      0    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      0      0      0      0    
TOATYP_2           0      0      0      0      0      0      0      0    
                  LOC_34 LOC_35 LOC_36 LOC_37 LOC_38 LOC_39 LOC_40 LOC_41
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1     -1     -1     -1     -1    
TOA_48             0      0      0      0      0      0      0      0    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      0      0      0      0    
TOATYP_2           0      0      0      0      0      0      0      0    
                  LOC_42 LOC_44 LOC_45 LOC_46 LOC_47 LOC_48 LOC_49 LOC_50
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1     -1     -1     -1     -1    
TOA_48             0      0      0      0      0      0      0      0    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      0      0      0      0    
TOATYP_2           0      0      0      0      0      0      0      0    
                  LOC_51 LOC_53 LOC_54 LOC_55 TOA_10 TOA_15 TOA_20 TOA_30
YearsToRetirement  0      0      0      0      0      0      0      0    
AGELVL_J           0      0      0      0      0      0      0      0    
AGELVL_K           0      0      0      0      0      0      0      0    
LOC_56            -1     -1     -1     -1      0      0      0      0    
TOA_48             0      0      0      0     -1     -1     -1     -1    
PPGROUP_12         0      0      0      0      0      0      0      0    
TOATYP_1           0      0      0      0      1      1      0      1    
TOATYP_2           0      0      0      0     -1     -1      0     -1    
                  TOA_32 TOA_35 TOA_38 TOA_40 TOA_42 TOA_44 TOA_45 PPGROUP_11
YearsToRetirement  0      0      0      0      0      0      0      0        
AGELVL_J           0      0      0      0      0      0      0      0        
AGELVL_K           0      0      0      0      0      0      0      0        
LOC_56             0      0      0      0      0      0      0      0        
TOA_48            -1     -1     -1     -1     -1     -1     -1      0        
PPGROUP_12         0      0      0      0      0      0      0     -1        
TOATYP_1           1      1      1      0      0      0      0      0        
TOATYP_2          -1     -1     -1      0      0      0      0      0        

In [134]:
%%R
tmp <- alias(BinLogit)$Complete
#print(attributes(tmp))
aliased <- dimnames(tmp)[[1]]
In [135]:
%%R
#aliased <- ifelse(grepl('[[:digit:]]$', aliased), substr(aliased, 1, nchar(aliased)-1), aliased)
print(c("Following attributes will be dropped from model due to multicollinearity:", aliased))

paste(as.character(length(vars) - length(vars[!vars %in% c(aliased)])), "attributes removed from model input")
[1] "Following attributes will be dropped from model due to multicollinearity:"
[2] "YearsToRetirement"                                                        
[3] "AGELVL_J"                                                                 
[4] "AGELVL_K"                                                                 
[5] "LOC_56"                                                                   
[6] "TOA_48"                                                                   
[7] "PPGROUP_12"                                                               
[8] "TOATYP_1"                                                                 
[9] "TOATYP_2"                                                                 
[1] "8 attributes removed from model input"
In [136]:
%%R
runLogit <- function(less, vars){
    fla <- paste("SEP ~", paste(vars, collapse="+"))
    fla <- as.formula(fla)

    binLog <- glm(fla, data = OPMAnalysisDataNoFamBinary, family = "binomial")
    return(binLog)
}

vars <- vars[!(vars %in% c(aliased))]
BinLogit2 <- runLogit(aliased, vars)
summary(BinLogit2)
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0182  -0.6661  -0.1038   0.7300   3.3486  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    -4.197348   1.576487  -2.662 0.007757 ** 
GSEGRD                         -4.648744   0.378877 -12.270  < 2e-16 ***
IndAvgSalary                   -2.226607   0.846708  -2.630 0.008545 ** 
SalaryOverUnderIndAvg          -0.439162   1.619677  -0.271 0.786282    
LowerLimitAge                  -2.540729   1.212200  -2.096 0.036085 *  
BLS_FEDERAL_OtherSep_Rate      17.409681   1.876284   9.279  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         32.683546   2.923824  11.178  < 2e-16 ***
BLS_FEDERAL_TotalSep_Level    -11.140742   9.295826  -1.198 0.230735    
BLS_FEDERAL_JobOpenings_Rate  -28.952564   7.797843  -3.713 0.000205 ***
BLS_FEDERAL_OtherSep_Level    -13.160928   1.193940 -11.023  < 2e-16 ***
BLS_FEDERAL_Quits_Level       -16.573381   2.709025  -6.118 9.49e-10 ***
BLS_FEDERAL_JobOpenings_Level  26.061857   7.228661   3.605 0.000312 ***
BLS_FEDERAL_Layoffs_Rate      -48.541013   3.573051 -13.585  < 2e-16 ***
BLS_FEDERAL_Layoffs_Level      91.075716   9.095455  10.013  < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate     -37.146001   3.619372 -10.263  < 2e-16 ***
SALARYLog                       0.778065   1.458424   0.533 0.593689    
LOSSqrt                        -6.546830   0.205469 -31.863  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.385652   0.111369  -3.463 0.000534 ***
SEPCount_EFDATE_LOCLog         -2.285935   0.658082  -3.474 0.000513 ***
IndAvgSalaryLog                 7.153809   1.471637   4.861 1.17e-06 ***
AGELVL_B                       -0.986586   1.153840  -0.855 0.392526    
AGELVL_C                       -0.374074   0.999550  -0.374 0.708224    
AGELVL_D                       -0.153536   0.864342  -0.178 0.859011    
AGELVL_E                        0.169895   0.730631   0.233 0.816125    
AGELVL_F                        0.425987   0.597625   0.713 0.475969    
AGELVL_G                        0.719669   0.465324   1.547 0.121961    
AGELVL_H                        0.878826   0.334395   2.628 0.008586 ** 
AGELVL_I                        0.751104   0.208116   3.609 0.000307 ***
LOC_01                          0.046480   0.414445   0.112 0.910704    
LOC_02                          0.170594   0.449780   0.379 0.704477    
LOC_04                          0.884459   0.419602   2.108 0.035044 *  
LOC_05                          0.127465   0.434442   0.293 0.769216    
LOC_06                          1.441664   0.498656   2.891 0.003839 ** 
LOC_08                          0.619457   0.424039   1.461 0.144057    
LOC_09                         -0.479861   0.498851  -0.962 0.336083    
LOC_10                         -0.571930   0.719466  -0.795 0.426650    
LOC_11                          0.854293   0.482586   1.770 0.076687 .  
LOC_12                          0.730602   0.437520   1.670 0.094945 .  
LOC_13                          0.767622   0.428145   1.793 0.072989 .  
LOC_15                         -0.012564   0.405718  -0.031 0.975296    
LOC_16                          0.104116   0.457768   0.227 0.820080    
LOC_17                          0.222340   0.418562   0.531 0.595280    
LOC_18                         -0.040707   0.445074  -0.091 0.927127    
LOC_19                          0.002682   0.494368   0.005 0.995671    
LOC_20                          0.713290   0.450296   1.584 0.113183    
LOC_21                          0.121747   0.424599   0.287 0.774316    
LOC_22                         -0.061715   0.421059  -0.147 0.883470    
LOC_23                          0.032093   0.505883   0.063 0.949416    
LOC_24                          0.703076   0.456910   1.539 0.123862    
LOC_25                          0.330490   0.419391   0.788 0.430683    
LOC_26                          0.160693   0.422700   0.380 0.703828    
LOC_27                          0.517693   0.443432   1.167 0.243021    
LOC_28                          0.109315   0.441527   0.248 0.804456    
LOC_29                          0.200044   0.436890   0.458 0.647037    
LOC_30                          0.714635   0.440294   1.623 0.104571    
LOC_31                         -0.100029   0.496424  -0.201 0.840309    
LOC_32                          0.266571   0.446144   0.598 0.550173    
LOC_33                         -0.268449   0.648422  -0.414 0.678872    
LOC_34                          0.001309   0.419181   0.003 0.997509    
LOC_35                          0.747138   0.407297   1.834 0.066598 .  
LOC_36                          0.562931   0.438910   1.283 0.199644    
LOC_37                          0.540114   0.422464   1.278 0.201079    
LOC_38                          0.316471   0.520899   0.608 0.543487    
LOC_39                          0.274637   0.418926   0.656 0.512098    
LOC_40                          0.449613   0.413569   1.087 0.276969    
LOC_41                          0.472063   0.430142   1.097 0.272440    
LOC_42                          0.241938   0.434815   0.556 0.577926    
LOC_44                         -0.354656   0.667342  -0.531 0.595110    
LOC_45                          0.198677   0.430252   0.462 0.644247    
LOC_46                          0.541219   0.446046   1.213 0.224988    
LOC_47                          0.048904   0.435216   0.112 0.910533    
LOC_48                          1.274278   0.477731   2.667 0.007645 ** 
LOC_49                          0.233045   0.433959   0.537 0.591254    
LOC_50                         -0.465553   0.711575  -0.654 0.512947    
LOC_51                          0.619192   0.471212   1.314 0.188832    
LOC_53                          0.941819   0.432155   2.179 0.029305 *  
LOC_54                         -0.098544   0.454643  -0.217 0.828404    
LOC_55                         -0.258949   0.446015  -0.581 0.561521    
TOA_10                         -1.147810   0.208103  -5.516 3.48e-08 ***
TOA_15                         -1.078670   0.213121  -5.061 4.16e-07 ***
TOA_20                          0.015746   0.280101   0.056 0.955171    
TOA_30                         -0.713454   0.223705  -3.189 0.001426 ** 
TOA_32                         -0.349741   0.799848  -0.437 0.661924    
TOA_35                         -1.502739   0.361130  -4.161 3.17e-05 ***
TOA_38                         -0.746850   0.213613  -3.496 0.000472 ***
TOA_40                         -1.049221   0.270570  -3.878 0.000105 ***
TOA_42                          0.064794   0.489778   0.132 0.894753    
TOA_44                          1.317649   1.130979   1.165 0.243998    
TOA_45                          8.104085 139.230042   0.058 0.953584    
PPGROUP_11                      0.379866   0.158079   2.403 0.016261 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13443  on 14830  degrees of freedom
AIC: 13623

Number of Fisher Scoring iterations: 10

In [137]:
%%R

#install.packages("car")
#require(car)
library(car)
runVifs <- function(logit){
    tmp <- as.data.frame(vif(logit))
    colnames(tmp) <- "VIF"

    scipen.default <- getOption("scipen")
    options(scipen=999)
    print(tmp)
    options(scipen=scipen.default)
    return(tmp)
}

vifs.BinLogit2 <- runVifs(BinLogit2)
                                       VIF
GSEGRD                           14.110175
IndAvgSalary                     42.900149
SalaryOverUnderIndAvg             9.579701
LowerLimitAge                   219.325838
BLS_FEDERAL_OtherSep_Rate       564.651743
BLS_FEDERAL_Quits_Rate         1052.281028
BLS_FEDERAL_TotalSep_Level    10461.617797
BLS_FEDERAL_JobOpenings_Rate  12261.146763
BLS_FEDERAL_OtherSep_Level      207.403680
BLS_FEDERAL_Quits_Level        1106.678602
BLS_FEDERAL_JobOpenings_Level 11662.910265
BLS_FEDERAL_Layoffs_Rate       1769.294407
BLS_FEDERAL_Layoffs_Level     10430.725186
BLS_FEDERAL_TotalSep_Rate      1755.735030
SALARYLog                       102.628189
LOSSqrt                           1.973772
SEPCount_EFDATE_OCCLog            1.123983
SEPCount_EFDATE_LOCLog           24.988435
IndAvgSalaryLog                 142.077732
AGELVL_B                         27.744404
AGELVL_C                        207.457982
AGELVL_D                        237.573778
AGELVL_E                        149.529939
AGELVL_F                         83.990092
AGELVL_G                         45.565410
AGELVL_H                         23.764489
AGELVL_I                          9.371740
LOC_01                            6.040791
LOC_02                            2.917066
LOC_04                            9.579683
LOC_05                            3.215149
LOC_06                           37.535884
LOC_08                           11.672734
LOC_09                            2.212192
LOC_10                            1.396902
LOC_11                           53.702314
LOC_12                           14.250681
LOC_13                           13.959157
LOC_15                            6.228701
LOC_16                            2.652246
LOC_17                            8.129706
LOC_18                            3.089854
LOC_19                            2.145980
LOC_20                            2.848711
LOC_21                            4.170515
LOC_22                            3.983023
LOC_23                            2.097936
LOC_24                           35.724766
LOC_25                            4.990639
LOC_26                            4.449805
LOC_27                            3.154766
LOC_28                            3.109605
LOC_29                            6.115997
LOC_30                            3.273054
LOC_31                            2.105127
LOC_32                            2.898692
LOC_33                            1.515369
LOC_34                            4.399908
LOC_35                            7.413789
LOC_36                            9.518652
LOC_37                            7.692933
LOC_38                            1.947582
LOC_39                            8.816013
LOC_40                            6.097166
LOC_41                            4.719139
LOC_42                            9.197718
LOC_44                            1.479747
LOC_45                            3.624529
LOC_46                            2.879513
LOC_47                            4.013711
LOC_48                           29.433241
LOC_49                            4.168211
LOC_50                            1.409060
LOC_51                           41.609055
LOC_53                           11.631298
LOC_54                            2.701110
LOC_55                            2.991687
TOA_10                           22.507735
TOA_15                           12.146029
TOA_20                            2.121598
TOA_30                            7.378587
TOA_32                            1.077935
TOA_35                            1.553062
TOA_38                           10.937132
TOA_40                            2.369396
TOA_42                            1.216934
TOA_44                            1.039689
TOA_45                            1.000003
PPGROUP_11                        1.895725
In [138]:
%%R

vifs.BinLogitRepeat <- vifs.BinLogit2
vars.Repeat <- vars

vif.removed <- vector(mode="character", length=0)
ndev.vect <- vector(mode="character", length=0)
ndf.vect <- vector(mode="character", length=0)
pchisq.vect <- vector(mode="character", length=0)
logLik.vect <- vector(mode="character", length=0)
AIC.vect <- vector(mode="character", length=0)
BIC.vect <- vector(mode="character", length=0)


for(i in seq(1,13)){
    remove <- rownames(vifs.BinLogitRepeat)[which.max(vifs.BinLogitRepeat$VIF)]
    vif.removed <- c(vif.removed, remove)
    cat("\n\n\nRemoved BEFORE this step:", remove, "\n")
    vars.Repeat <- vars.Repeat[!(vars.Repeat %in% c(remove))]
    BinLogitRepeat <- runLogit(remove, vars.Repeat)
    print(summary(BinLogitRepeat))
    vifs.BinLogitRepeat <- runVifs(BinLogitRepeat)
    
    ##goodness of fit
    ndev.vect <- c(ndev.vect, with(BinLogitRepeat, null.deviance - deviance))
    ndf.vect <- c(ndf.vect, with(BinLogitRepeat, df.null - df.residual))
    pchisq.vect <- c(pchisq.vect, with(BinLogitRepeat, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE)))
    logLik.vect <- c(logLik.vect, logLik(BinLogitRepeat))
    AIC.vect <- c(AIC.vect, AIC(BinLogitRepeat))
    BIC.vect <- c(BIC.vect, BIC(BinLogitRepeat))
}
cat("\nFollowing variables removed based on VIF values (in order of removal):\n")
print(vif.removed)

cat("\n\nNull Deviances (in order):\n")
print(ndev.vect)
cat("\nMin value at iteration = ", which.min(ndev.vect))

cat("\n\nDiff Degrees of Freedom (in order):\n")
print(ndf.vect)
cat("\nMin value at iteration = ", which.min(ndf.vect))

#cat("\n\nP-ChiSquare (in order):\n")
#print(pchisq.vect)
#cat("\nMin value at iteration = ", which.min(pchisq.vect))

cat("\n\nLog Likelihoods (in order):\n")
print(logLik.vect)
cat("\nMin value at iteration = ", which.min(logLik.vect))

cat("\n\nAIC values (in order):\n")
print(AIC.vect)
cat("\nMin value at iteration = ", which.min(AIC.vect))

cat("\n\nBIC values (in order):\n")
print(BIC.vect)
cat("\nMin value at iteration = ", which.min(BIC.vect))


Removed BEFORE this step: BLS_FEDERAL_JobOpenings_Rate 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0254  -0.6709  -0.1042   0.7323   3.3514  

Coefficients:
                               Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    -2.42850    1.50280  -1.616 0.106099    
GSEGRD                         -4.64944    0.37864 -12.279  < 2e-16 ***
IndAvgSalary                   -2.25204    0.84603  -2.662 0.007770 ** 
SalaryOverUnderIndAvg          -0.47750    1.61984  -0.295 0.768161    
LowerLimitAge                  -2.52350    1.21163  -2.083 0.037275 *  
BLS_FEDERAL_OtherSep_Rate      10.64809    0.43711  24.360  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         26.42436    2.37950  11.105  < 2e-16 ***
BLS_FEDERAL_TotalSep_Level     15.34868    5.94666   2.581 0.009850 ** 
BLS_FEDERAL_OtherSep_Level    -13.49154    1.19077 -11.330  < 2e-16 ***
BLS_FEDERAL_Quits_Level       -18.20781    2.67227  -6.814 9.52e-12 ***
BLS_FEDERAL_JobOpenings_Level  -0.76522    0.22629  -3.382 0.000721 ***
BLS_FEDERAL_Layoffs_Rate      -42.20793    3.13023 -13.484  < 2e-16 ***
BLS_FEDERAL_Layoffs_Level      59.09779    2.84443  20.777  < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate     -29.03054    2.89164 -10.039  < 2e-16 ***
SALARYLog                       0.80129    1.45821   0.550 0.582662    
LOSSqrt                        -6.54585    0.20539 -31.871  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.39228    0.11130  -3.525 0.000424 ***
SEPCount_EFDATE_LOCLog         -2.42118    0.65639  -3.689 0.000225 ***
IndAvgSalaryLog                 7.14723    1.47053   4.860 1.17e-06 ***
AGELVL_B                       -0.96630    1.15318  -0.838 0.402062    
AGELVL_C                       -0.35773    0.99899  -0.358 0.720271    
AGELVL_D                       -0.13959    0.86385  -0.162 0.871624    
AGELVL_E                        0.18129    0.73020   0.248 0.803925    
AGELVL_F                        0.43488    0.59726   0.728 0.466540    
AGELVL_G                        0.72932    0.46502   1.568 0.116798    
AGELVL_H                        0.88527    0.33414   2.649 0.008064 ** 
AGELVL_I                        0.75321    0.20794   3.622 0.000292 ***
LOC_01                          0.07559    0.41483   0.182 0.855401    
LOC_02                          0.18813    0.45005   0.418 0.675928    
LOC_04                          0.93188    0.41977   2.220 0.026421 *  
LOC_05                          0.12762    0.43515   0.293 0.769306    
LOC_06                          1.51868    0.49825   3.048 0.002303 ** 
LOC_08                          0.66864    0.42424   1.576 0.115006    
LOC_09                         -0.46603    0.49952  -0.933 0.350843    
LOC_10                         -0.62563    0.72085  -0.868 0.385444    
LOC_11                          0.92905    0.48226   1.926 0.054046 .  
LOC_12                          0.77994    0.43762   1.782 0.074714 .  
LOC_13                          0.81121    0.42833   1.894 0.058237 .  
LOC_15                          0.02002    0.40613   0.049 0.960677    
LOC_16                          0.13202    0.45897   0.288 0.773621    
LOC_17                          0.25677    0.41883   0.613 0.539833    
LOC_18                         -0.01860    0.44583  -0.042 0.966724    
LOC_19                         -0.00628    0.49500  -0.013 0.989877    
LOC_20                          0.74183    0.45090   1.645 0.099921 .  
LOC_21                          0.16093    0.42496   0.379 0.704920    
LOC_22                         -0.04295    0.42157  -0.102 0.918847    
LOC_23                          0.04034    0.50584   0.080 0.936434    
LOC_24                          0.77175    0.45672   1.690 0.091071 .  
LOC_25                          0.35921    0.41979   0.856 0.392177    
LOC_26                          0.17583    0.42304   0.416 0.677683    
LOC_27                          0.53713    0.44380   1.210 0.226161    
LOC_28                          0.11988    0.44202   0.271 0.786223    
LOC_29                          0.24948    0.43701   0.571 0.568078    
LOC_30                          0.73906    0.44069   1.677 0.093535 .  
LOC_31                         -0.07783    0.49642  -0.157 0.875415    
LOC_32                          0.29732    0.44586   0.667 0.504872    
LOC_33                         -0.31393    0.64887  -0.484 0.628520    
LOC_34                          0.01808    0.41964   0.043 0.965627    
LOC_35                          0.77699    0.40768   1.906 0.056668 .  
LOC_36                          0.61237    0.43905   1.395 0.163089    
LOC_37                          0.57193    0.42268   1.353 0.176017    
LOC_38                          0.30715    0.52123   0.589 0.555670    
LOC_39                          0.31256    0.41929   0.745 0.455992    
LOC_40                          0.49256    0.41378   1.190 0.233891    
LOC_41                          0.50618    0.43042   1.176 0.239583    
LOC_42                          0.29690    0.43493   0.683 0.494841    
LOC_44                         -0.37269    0.66585  -0.560 0.575668    
LOC_45                          0.21721    0.43066   0.504 0.613998    
LOC_46                          0.54344    0.44620   1.218 0.223253    
LOC_47                          0.08097    0.43560   0.186 0.852531    
LOC_48                          1.34257    0.47749   2.812 0.004928 ** 
LOC_49                          0.27280    0.43373   0.629 0.529372    
LOC_50                         -0.50843    0.71201  -0.714 0.475178    
LOC_51                          0.68773    0.47099   1.460 0.144243    
LOC_53                          0.99034    0.43242   2.290 0.022008 *  
LOC_54                         -0.09079    0.45508  -0.200 0.841862    
LOC_55                         -0.24139    0.44658  -0.541 0.588839    
TOA_10                         -1.13868    0.20787  -5.478 4.31e-08 ***
TOA_15                         -1.06790    0.21288  -5.017 5.26e-07 ***
TOA_20                          0.02039    0.27975   0.073 0.941895    
TOA_30                         -0.69944    0.22344  -3.130 0.001746 ** 
TOA_32                         -0.32065    0.80461  -0.399 0.690249    
TOA_35                         -1.48994    0.36090  -4.128 3.65e-05 ***
TOA_38                         -0.73771    0.21336  -3.457 0.000545 ***
TOA_40                         -1.03510    0.27048  -3.827 0.000130 ***
TOA_42                          0.07756    0.48936   0.158 0.874063    
TOA_44                          1.32302    1.13099   1.170 0.242085    
TOA_45                          8.12598  139.25915   0.058 0.953469    
PPGROUP_11                      0.37123    0.15780   2.353 0.018644 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13457  on 14831  degrees of freedom
AIC: 13635

Number of Fisher Scoring iterations: 10

                                      VIF
GSEGRD                          14.102411
IndAvgSalary                    42.867169
SalaryOverUnderIndAvg            9.596934
LowerLimitAge                  219.397663
BLS_FEDERAL_OtherSep_Rate       30.490313
BLS_FEDERAL_Quits_Rate         698.268418
BLS_FEDERAL_TotalSep_Level    4264.767790
BLS_FEDERAL_OtherSep_Level     204.909502
BLS_FEDERAL_Quits_Level       1081.666363
BLS_FEDERAL_JobOpenings_Level   11.391217
BLS_FEDERAL_Layoffs_Rate      1354.785981
BLS_FEDERAL_Layoffs_Level     1017.591476
BLS_FEDERAL_TotalSep_Rate     1117.131916
SALARYLog                      102.663330
LOSSqrt                          1.974899
SEPCount_EFDATE_OCCLog           1.123968
SEPCount_EFDATE_LOCLog          24.878470
IndAvgSalaryLog                141.984104
AGELVL_B                        27.781513
AGELVL_C                       207.533042
AGELVL_D                       237.591178
AGELVL_E                       149.593271
AGELVL_F                        83.959156
AGELVL_G                        45.563783
AGELVL_H                        23.767317
AGELVL_I                         9.363424
LOC_01                           6.064846
LOC_02                           2.930911
LOC_04                           9.618857
LOC_05                           3.221621
LOC_06                          37.559058
LOC_08                          11.704767
LOC_09                           2.216146
LOC_10                           1.396248
LOC_11                          53.613146
LOC_12                          14.284626
LOC_13                          13.978394
LOC_15                           6.251134
LOC_16                           2.646779
LOC_17                           8.166015
LOC_18                           3.090363
LOC_19                           2.150571
LOC_20                           2.853493
LOC_21                           4.184083
LOC_22                           3.997274
LOC_23                           2.108420
LOC_24                          35.764621
LOC_25                           5.010330
LOC_26                           4.475522
LOC_27                           3.169450
LOC_28                           3.120200
LOC_29                           6.125775
LOC_30                           3.287498
LOC_31                           2.114779
LOC_32                           2.926428
LOC_33                           1.516780
LOC_34                           4.418664
LOC_35                           7.448987
LOC_36                           9.534560
LOC_37                           7.737850
LOC_38                           1.953249
LOC_39                           8.819970
LOC_40                           6.133611
LOC_41                           4.741161
LOC_42                           9.214579
LOC_44                           1.485738
LOC_45                           3.639638
LOC_46                           2.899271
LOC_47                           4.023972
LOC_48                          29.418807
LOC_49                           4.212852
LOC_50                           1.410219
LOC_51                          41.569863
LOC_53                          11.610658
LOC_54                           2.711236
LOC_55                           2.998441
TOA_10                          22.487254
TOA_15                          12.132765
TOA_20                           2.122214
TOA_30                           7.366183
TOA_32                           1.076719
TOA_35                           1.552401
TOA_38                          10.935927
TOA_40                           2.363610
TOA_42                           1.216749
TOA_44                           1.039597
TOA_45                           1.000003
PPGROUP_11                       1.894311



Removed BEFORE this step: BLS_FEDERAL_TotalSep_Level 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0328  -0.6692  -0.1043   0.7330   3.3237  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    -2.031023   1.495067  -1.358 0.174311    
GSEGRD                         -4.642025   0.378572 -12.262  < 2e-16 ***
IndAvgSalary                   -2.290889   0.845951  -2.708 0.006768 ** 
SalaryOverUnderIndAvg          -0.494276   1.620122  -0.305 0.760301    
LowerLimitAge                  -2.508767   1.211764  -2.070 0.038420 *  
BLS_FEDERAL_OtherSep_Rate      10.832292   0.431229  25.120  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         21.809953   1.565644  13.930  < 2e-16 ***
BLS_FEDERAL_OtherSep_Level    -10.772107   0.547414 -19.678  < 2e-16 ***
BLS_FEDERAL_Quits_Level       -12.072981   1.218512  -9.908  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level  -0.396224   0.175826  -2.254 0.024227 *  
BLS_FEDERAL_Layoffs_Rate      -35.730424   1.863136 -19.178  < 2e-16 ***
BLS_FEDERAL_Layoffs_Level      57.394947   2.766181  20.749  < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate     -22.146198   1.099969 -20.133  < 2e-16 ***
SALARYLog                       0.817352   1.458061   0.561 0.575088    
LOSSqrt                        -6.550308   0.205412 -31.889  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.388358   0.111293  -3.489 0.000484 ***
SEPCount_EFDATE_LOCLog         -1.893710   0.624563  -3.032 0.002429 ** 
IndAvgSalaryLog                 7.157486   1.470497   4.867 1.13e-06 ***
AGELVL_B                       -0.956914   1.153207  -0.830 0.406660    
AGELVL_C                       -0.347971   0.999055  -0.348 0.727615    
AGELVL_D                       -0.133566   0.863902  -0.155 0.877130    
AGELVL_E                        0.185230   0.730237   0.254 0.799760    
AGELVL_F                        0.438454   0.597280   0.734 0.462897    
AGELVL_G                        0.730103   0.465026   1.570 0.116409    
AGELVL_H                        0.883120   0.334135   2.643 0.008217 ** 
AGELVL_I                        0.753904   0.207935   3.626 0.000288 ***
LOC_01                         -0.024893   0.413278  -0.060 0.951969    
LOC_02                          0.151033   0.449846   0.336 0.737064    
LOC_04                          0.802625   0.417008   1.925 0.054264 .  
LOC_05                          0.112331   0.435095   0.258 0.796271    
LOC_06                          1.251349   0.487791   2.565 0.010307 *  
LOC_08                          0.525700   0.420909   1.249 0.211678    
LOC_09                         -0.406109   0.499046  -0.814 0.415776    
LOC_10                         -0.535558   0.718911  -0.745 0.456298    
LOC_11                          0.677568   0.472808   1.433 0.151837    
LOC_12                          0.605149   0.432713   1.399 0.161963    
LOC_13                          0.640478   0.423533   1.512 0.130476    
LOC_15                         -0.043014   0.405598  -0.106 0.915541    
LOC_16                          0.103847   0.458943   0.226 0.820988    
LOC_17                          0.135258   0.416475   0.325 0.745356    
LOC_18                         -0.075910   0.445593  -0.170 0.864729    
LOC_19                          0.023551   0.494861   0.048 0.962042    
LOC_20                          0.714518   0.450851   1.585 0.113007    
LOC_21                          0.092662   0.424267   0.218 0.827113    
LOC_22                         -0.090927   0.421307  -0.216 0.829126    
LOC_23                          0.083722   0.505214   0.166 0.868380    
LOC_24                          0.553956   0.449335   1.233 0.217637    
LOC_25                          0.278371   0.418860   0.665 0.506310    
LOC_26                          0.097189   0.422185   0.230 0.817932    
LOC_27                          0.477322   0.443580   1.076 0.281896    
LOC_28                          0.084831   0.441741   0.192 0.847713    
LOC_29                          0.109975   0.433947   0.253 0.799936    
LOC_30                          0.677027   0.440178   1.538 0.124030    
LOC_31                         -0.071506   0.496297  -0.144 0.885438    
LOC_32                          0.264286   0.445908   0.593 0.553388    
LOC_33                         -0.227378   0.649299  -0.350 0.726196    
LOC_34                         -0.040644   0.419062  -0.097 0.922736    
LOC_35                          0.687215   0.406450   1.691 0.090881 .  
LOC_36                          0.441608   0.434411   1.017 0.309360    
LOC_37                          0.440051   0.419863   1.048 0.294601    
LOC_38                          0.338562   0.522099   0.648 0.516685    
LOC_39                          0.185199   0.416714   0.444 0.656733    
LOC_40                          0.404623   0.412679   0.980 0.326849    
LOC_41                          0.405750   0.428802   0.946 0.344025    
LOC_42                          0.139071   0.430897   0.323 0.746886    
LOC_44                         -0.271049   0.664132  -0.408 0.683182    
LOC_45                          0.163522   0.430260   0.380 0.703905    
LOC_46                          0.547586   0.446033   1.228 0.219567    
LOC_47                         -0.008055   0.434508  -0.019 0.985210    
LOC_48                          1.099952   0.468606   2.347 0.018911 *  
LOC_49                          0.183170   0.432716   0.423 0.672075    
LOC_50                         -0.412373   0.707674  -0.583 0.560085    
LOC_51                          0.449485   0.462350   0.972 0.330963    
LOC_53                          0.825464   0.428033   1.929 0.053792 .  
LOC_54                         -0.103560   0.454805  -0.228 0.819877    
LOC_55                         -0.288723   0.446207  -0.647 0.517593    
TOA_10                         -1.139626   0.207847  -5.483 4.18e-08 ***
TOA_15                         -1.068862   0.212838  -5.022 5.11e-07 ***
TOA_20                          0.019372   0.279736   0.069 0.944790    
TOA_30                         -0.699710   0.223449  -3.131 0.001740 ** 
TOA_32                         -0.340639   0.804583  -0.423 0.672023    
TOA_35                         -1.487324   0.361021  -4.120 3.79e-05 ***
TOA_38                         -0.737305   0.213332  -3.456 0.000548 ***
TOA_40                         -1.027798   0.270302  -3.802 0.000143 ***
TOA_42                          0.079011   0.489497   0.161 0.871768    
TOA_44                          1.331265   1.131143   1.177 0.239227    
TOA_45                          8.161669 139.057878   0.059 0.953197    
PPGROUP_11                      0.363968   0.157802   2.306 0.021084 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13464  on 14832  degrees of freedom
AIC: 13640

Number of Fisher Scoring iterations: 10

                                     VIF
GSEGRD                         14.096450
IndAvgSalary                   42.844549
SalaryOverUnderIndAvg           9.594943
LowerLimitAge                 219.535659
BLS_FEDERAL_OtherSep_Rate      29.584276
BLS_FEDERAL_Quits_Rate        300.982891
BLS_FEDERAL_OtherSep_Level     43.194480
BLS_FEDERAL_Quits_Level       223.370966
BLS_FEDERAL_JobOpenings_Level   6.852760
BLS_FEDERAL_Layoffs_Rate      479.157701
BLS_FEDERAL_Layoffs_Level     961.396403
BLS_FEDERAL_TotalSep_Rate     161.354837
SALARYLog                     102.645636
LOSSqrt                         1.974931
SEPCount_EFDATE_OCCLog          1.123858
SEPCount_EFDATE_LOCLog         22.542581
IndAvgSalaryLog               141.961235
AGELVL_B                       27.854483
AGELVL_C                      207.826584
AGELVL_D                      237.746047
AGELVL_E                      149.661959
AGELVL_F                       84.060896
AGELVL_G                       45.615524
AGELVL_H                       23.780677
AGELVL_I                        9.360065
LOC_01                          6.017956
LOC_02                          2.935738
LOC_04                          9.525681
LOC_05                          3.231022
LOC_06                         36.005770
LOC_08                         11.534754
LOC_09                          2.214122
LOC_10                          1.395777
LOC_11                         51.513697
LOC_12                         13.994128
LOC_13                         13.681734
LOC_15                          6.251670
LOC_16                          2.648036
LOC_17                          8.078119
LOC_18                          3.082752
LOC_19                          2.152426
LOC_20                          2.857458
LOC_21                          4.178398
LOC_22                          3.997988
LOC_23                          2.112516
LOC_24                         34.606682
LOC_25                          4.988039
LOC_26                          4.458787
LOC_27                          3.158467
LOC_28                          3.128964
LOC_29                          6.056461
LOC_30                          3.277764
LOC_31                          2.118724
LOC_32                          2.925017
LOC_33                          1.510859
LOC_34                          4.423038
LOC_35                          7.410529
LOC_36                          9.336726
LOC_37                          7.638325
LOC_38                          1.947152
LOC_39                          8.704343
LOC_40                          6.097745
LOC_41                          4.710050
LOC_42                          9.070910
LOC_44                          1.482675
LOC_45                          3.640281
LOC_46                          2.910038
LOC_47                          4.004226
LOC_48                         28.340768
LOC_49                          4.180681
LOC_50                          1.413411
LOC_51                         40.067925
LOC_53                         11.382561
LOC_54                          2.722130
LOC_55                          3.001285
TOA_10                         22.494780
TOA_15                         12.133793
TOA_20                          2.122048
TOA_30                          7.365433
TOA_32                          1.076605
TOA_35                          1.551310
TOA_38                         10.941032
TOA_40                          2.367088
TOA_42                          1.216410
TOA_44                          1.039596
TOA_45                          1.000003
PPGROUP_11                      1.893480



Removed BEFORE this step: BLS_FEDERAL_Layoffs_Level 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0115  -0.7000  -0.1278   0.7851   3.3366  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     4.415400   1.446904   3.052 0.002276 ** 
GSEGRD                         -4.476059   0.371748 -12.041  < 2e-16 ***
IndAvgSalary                   -2.407166   0.832846  -2.890 0.003849 ** 
SalaryOverUnderIndAvg          -0.318951   1.606141  -0.199 0.842590    
LowerLimitAge                  -2.513818   1.198888  -2.097 0.036012 *  
BLS_FEDERAL_OtherSep_Rate       4.321756   0.283369  15.251  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         -8.663824   0.565563 -15.319  < 2e-16 ***
BLS_FEDERAL_OtherSep_Level     -2.741609   0.366784  -7.475 7.74e-14 ***
BLS_FEDERAL_Quits_Level        10.001527   0.617314  16.202  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   2.270524   0.123285  18.417  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        2.111878   0.365828   5.773 7.79e-09 ***
BLS_FEDERAL_TotalSep_Rate      -2.771317   0.568355  -4.876 1.08e-06 ***
SALARYLog                       0.577614   1.438593   0.402 0.688043    
LOSSqrt                        -6.454942   0.202125 -31.935  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.271992   0.108794  -2.500 0.012417 *  
SEPCount_EFDATE_LOCLog         -1.705988   0.609787  -2.798 0.005147 ** 
IndAvgSalaryLog                 7.216041   1.448733   4.981 6.33e-07 ***
AGELVL_B                       -0.961675   1.140194  -0.843 0.398987    
AGELVL_C                       -0.382936   0.988175  -0.388 0.698373    
AGELVL_D                       -0.202090   0.854451  -0.237 0.813033    
AGELVL_E                        0.106171   0.722170   0.147 0.883119    
AGELVL_F                        0.364012   0.590614   0.616 0.537678    
AGELVL_G                        0.633967   0.459735   1.379 0.167900    
AGELVL_H                        0.810652   0.330176   2.455 0.014080 *  
AGELVL_I                        0.699723   0.205390   3.407 0.000657 ***
LOC_01                         -0.111397   0.407303  -0.273 0.784470    
LOC_02                          0.125126   0.440758   0.284 0.776495    
LOC_04                          0.693870   0.410606   1.690 0.091053 .  
LOC_05                         -0.036468   0.427186  -0.085 0.931970    
LOC_06                          1.060841   0.479364   2.213 0.026896 *  
LOC_08                          0.409381   0.414095   0.989 0.322851    
LOC_09                         -0.418575   0.485426  -0.862 0.388531    
LOC_10                         -0.542713   0.705796  -0.769 0.441931    
LOC_11                          0.514070   0.464708   1.106 0.268631    
LOC_12                          0.419001   0.425701   0.984 0.324987    
LOC_13                          0.477845   0.416638   1.147 0.251420    
LOC_15                         -0.090656   0.399223  -0.227 0.820361    
LOC_16                         -0.036096   0.450961  -0.080 0.936203    
LOC_17                         -0.049236   0.409802  -0.120 0.904368    
LOC_18                         -0.205725   0.437989  -0.470 0.638567    
LOC_19                         -0.017620   0.482890  -0.036 0.970893    
LOC_20                          0.591723   0.441188   1.341 0.179854    
LOC_21                          0.008412   0.417216   0.020 0.983914    
LOC_22                         -0.191329   0.414183  -0.462 0.644123    
LOC_23                         -0.037017   0.498041  -0.074 0.940752    
LOC_24                          0.393013   0.441642   0.890 0.373525    
LOC_25                          0.213738   0.410813   0.520 0.602867    
LOC_26                          0.007013   0.416646   0.017 0.986570    
LOC_27                          0.325818   0.434410   0.750 0.453240    
LOC_28                         -0.100594   0.435340  -0.231 0.817261    
LOC_29                         -0.078225   0.427109  -0.183 0.854680    
LOC_30                          0.585889   0.435407   1.346 0.178428    
LOC_31                         -0.047884   0.488689  -0.098 0.921945    
LOC_32                          0.151221   0.436949   0.346 0.729279    
LOC_33                         -0.169509   0.645307  -0.263 0.792798    
LOC_34                         -0.128380   0.411486  -0.312 0.755048    
LOC_35                          0.540071   0.400461   1.349 0.177458    
LOC_36                          0.319495   0.427217   0.748 0.454550    
LOC_37                          0.325537   0.413452   0.787 0.431070    
LOC_38                          0.221635   0.514229   0.431 0.666465    
LOC_39                         -0.009349   0.410282  -0.023 0.981821    
LOC_40                          0.309808   0.405627   0.764 0.445001    
LOC_41                          0.293544   0.422925   0.694 0.487631    
LOC_42                          0.012507   0.424190   0.029 0.976477    
LOC_44                         -0.295722   0.647162  -0.457 0.647706    
LOC_45                          0.020514   0.424336   0.048 0.961442    
LOC_46                          0.581204   0.439672   1.322 0.186200    
LOC_47                         -0.125172   0.428177  -0.292 0.770029    
LOC_48                          0.883398   0.460410   1.919 0.055020 .  
LOC_49                          0.031498   0.425742   0.074 0.941023    
LOC_50                         -0.619889   0.698438  -0.888 0.374790    
LOC_51                          0.307443   0.454587   0.676 0.498843    
LOC_53                          0.720927   0.421493   1.710 0.087190 .  
LOC_54                         -0.247537   0.448119  -0.552 0.580680    
LOC_55                         -0.355151   0.438919  -0.809 0.418429    
TOA_10                         -1.122365   0.203052  -5.527 3.25e-08 ***
TOA_15                         -1.070324   0.208191  -5.141 2.73e-07 ***
TOA_20                          0.047446   0.274635   0.173 0.862841    
TOA_30                         -0.718486   0.218404  -3.290 0.001003 ** 
TOA_32                         -0.442490   0.810832  -0.546 0.585256    
TOA_35                         -1.554527   0.353731  -4.395 1.11e-05 ***
TOA_38                         -0.739776   0.208594  -3.546 0.000390 ***
TOA_40                         -1.025434   0.264847  -3.872 0.000108 ***
TOA_42                          0.106986   0.484630   0.221 0.825281    
TOA_44                          0.962529   1.098287   0.876 0.380817    
TOA_45                          8.184772 134.352902   0.061 0.951423    
PPGROUP_11                      0.273301   0.154849   1.765 0.077573 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13915  on 14833  degrees of freedom
AIC: 14089

Number of Fisher Scoring iterations: 10

                                     VIF
GSEGRD                         14.084820
IndAvgSalary                   42.946627
SalaryOverUnderIndAvg           9.659062
LowerLimitAge                 222.204956
BLS_FEDERAL_OtherSep_Rate      13.419915
BLS_FEDERAL_Quits_Rate         38.717795
BLS_FEDERAL_OtherSep_Level     20.402730
BLS_FEDERAL_Quits_Level        55.691958
BLS_FEDERAL_JobOpenings_Level   3.613481
BLS_FEDERAL_Layoffs_Rate       18.823252
BLS_FEDERAL_TotalSep_Rate      44.241110
SALARYLog                     103.354860
LOSSqrt                         1.954522
SEPCount_EFDATE_OCCLog          1.120809
SEPCount_EFDATE_LOCLog         22.385361
IndAvgSalaryLog               142.796733
AGELVL_B                       28.604589
AGELVL_C                      211.730999
AGELVL_D                      242.703461
AGELVL_E                      153.427433
AGELVL_F                       86.336131
AGELVL_G                       46.508626
AGELVL_H                       24.243731
AGELVL_I                        9.382183
LOC_01                          5.976635
LOC_02                          3.003151
LOC_04                          9.529767
LOC_05                          3.271838
LOC_06                         35.861969
LOC_08                         11.622783
LOC_09                          2.276671
LOC_10                          1.398664
LOC_11                         51.904733
LOC_12                         14.130208
LOC_13                         13.959043
LOC_15                          6.339311
LOC_16                          2.665564
LOC_17                          8.090489
LOC_18                          3.107284
LOC_19                          2.194706
LOC_20                          2.925662
LOC_21                          4.229631
LOC_22                          4.044529
LOC_23                          2.101939
LOC_24                         35.047823
LOC_25                          5.170394
LOC_26                          4.400034
LOC_27                          3.237243
LOC_28                          3.121063
LOC_29                          6.334707
LOC_30                          3.236488
LOC_31                          2.118286
LOC_32                          2.982528
LOC_33                          1.493842
LOC_34                          4.507625
LOC_35                          7.329880
LOC_36                          9.438289
LOC_37                          7.597933
LOC_38                          1.942086
LOC_39                          8.669860
LOC_40                          6.228079
LOC_41                          4.673255
LOC_42                          8.979397
LOC_44                          1.495281
LOC_45                          3.621144
LOC_46                          2.900952
LOC_47                          3.994933
LOC_48                         28.968764
LOC_49                          4.200175
LOC_50                          1.410371
LOC_51                         40.230985
LOC_53                         11.359629
LOC_54                          2.715109
LOC_55                          3.016934
TOA_10                         22.333791
TOA_15                         12.080290
TOA_20                          2.100723
TOA_30                          7.351089
TOA_32                          1.071734
TOA_35                          1.551303
TOA_38                         10.828279
TOA_40                          2.348290
TOA_42                          1.209936
TOA_44                          1.039635
TOA_45                          1.000003
PPGROUP_11                      1.882840



Removed BEFORE this step: AGELVL_D 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0113  -0.7006  -0.1276   0.7849   3.3311  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     4.152229   0.924559   4.491 7.09e-06 ***
GSEGRD                         -4.475824   0.371745 -12.040  < 2e-16 ***
IndAvgSalary                   -2.408182   0.832791  -2.892 0.003832 ** 
SalaryOverUnderIndAvg          -0.320425   1.606010  -0.200 0.841859    
LowerLimitAge                  -2.231879   0.124668 -17.903  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       4.321013   0.283353  15.250  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         -8.664920   0.565540 -15.322  < 2e-16 ***
BLS_FEDERAL_OtherSep_Level     -2.740868   0.366777  -7.473 7.85e-14 ***
BLS_FEDERAL_Quits_Level        10.002849   0.617283  16.205  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   2.270382   0.123285  18.416  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        2.112702   0.365813   5.775 7.68e-09 ***
BLS_FEDERAL_TotalSep_Rate      -2.771975   0.568352  -4.877 1.08e-06 ***
SALARYLog                       0.579835   1.438506   0.403 0.686888    
LOSSqrt                        -6.453579   0.202036 -31.943  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.271767   0.108795  -2.498 0.012490 *  
SEPCount_EFDATE_LOCLog         -1.708753   0.609715  -2.803 0.005070 ** 
IndAvgSalaryLog                 7.214691   1.448687   4.980 6.35e-07 ***
AGELVL_B                       -0.697590   0.230465  -3.027 0.002471 ** 
AGELVL_C                       -0.150152   0.087748  -1.711 0.087052 .  
AGELVL_E                        0.276198   0.069197   3.991 6.57e-05 ***
AGELVL_F                        0.502674   0.072106   6.971 3.14e-12 ***
AGELVL_G                        0.741233   0.075995   9.754  < 2e-16 ***
AGELVL_H                        0.886556   0.078229  11.333  < 2e-16 ***
AGELVL_I                        0.744231   0.082728   8.996  < 2e-16 ***
LOC_01                         -0.111464   0.407366  -0.274 0.784377    
LOC_02                          0.124904   0.440837   0.283 0.776921    
LOC_04                          0.693825   0.410666   1.690 0.091121 .  
LOC_05                         -0.037436   0.427218  -0.088 0.930172    
LOC_06                          1.061284   0.479424   2.214 0.026852 *  
LOC_08                          0.409992   0.414155   0.990 0.322200    
LOC_09                         -0.418067   0.485501  -0.861 0.389181    
LOC_10                         -0.542433   0.705845  -0.768 0.442198    
LOC_11                          0.514459   0.464767   1.107 0.268330    
LOC_12                          0.419187   0.425764   0.985 0.324844    
LOC_13                          0.477955   0.416700   1.147 0.251381    
LOC_15                         -0.091181   0.399275  -0.228 0.819362    
LOC_16                         -0.037637   0.450943  -0.083 0.933484    
LOC_17                         -0.049224   0.409862  -0.120 0.904404    
LOC_18                         -0.205657   0.438050  -0.469 0.638725    
LOC_19                         -0.017298   0.483022  -0.036 0.971433    
LOC_20                          0.591671   0.441215   1.341 0.179920    
LOC_21                          0.008037   0.417281   0.019 0.984633    
LOC_22                         -0.191930   0.414230  -0.463 0.643121    
LOC_23                         -0.037429   0.498156  -0.075 0.940107    
LOC_24                          0.393422   0.441701   0.891 0.373092    
LOC_25                          0.213085   0.410853   0.519 0.604011    
LOC_26                          0.007156   0.416710   0.017 0.986298    
LOC_27                          0.325070   0.434426   0.748 0.454295    
LOC_28                         -0.101276   0.435372  -0.233 0.816057    
LOC_29                         -0.078853   0.427153  -0.185 0.853542    
LOC_30                          0.586197   0.435483   1.346 0.178275    
LOC_31                         -0.048085   0.488780  -0.098 0.921633    
LOC_32                          0.150836   0.436987   0.345 0.729965    
LOC_33                         -0.169129   0.645437  -0.262 0.793292    
LOC_34                         -0.128823   0.411546  -0.313 0.754263    
LOC_35                          0.540570   0.400521   1.350 0.177123    
LOC_36                          0.319547   0.427280   0.748 0.454542    
LOC_37                          0.325735   0.413514   0.788 0.430859    
LOC_38                          0.220334   0.514231   0.428 0.668306    
LOC_39                         -0.009850   0.410335  -0.024 0.980849    
LOC_40                          0.309172   0.405674   0.762 0.445988    
LOC_41                          0.293625   0.422986   0.694 0.487575    
LOC_42                          0.012562   0.424249   0.030 0.976378    
LOC_44                         -0.295659   0.647217  -0.457 0.647803    
LOC_45                          0.020248   0.424397   0.048 0.961946    
LOC_46                          0.580206   0.439710   1.320 0.186996    
LOC_47                         -0.126131   0.428225  -0.295 0.768343    
LOC_48                          0.883935   0.460467   1.920 0.054902 .  
LOC_49                          0.030672   0.425771   0.072 0.942572    
LOC_50                         -0.619105   0.698763  -0.886 0.375617    
LOC_51                          0.307762   0.454648   0.677 0.498454    
LOC_53                          0.721000   0.421556   1.710 0.087205 .  
LOC_54                         -0.247802   0.448186  -0.553 0.580332    
LOC_55                         -0.356169   0.438953  -0.811 0.417133    
TOA_10                         -1.121872   0.203023  -5.526 3.28e-08 ***
TOA_15                         -1.069512   0.208143  -5.138 2.77e-07 ***
TOA_20                          0.048596   0.274573   0.177 0.859519    
TOA_30                         -0.718023   0.218377  -3.288 0.001009 ** 
TOA_32                         -0.442571   0.810537  -0.546 0.585051    
TOA_35                         -1.553844   0.353708  -4.393 1.12e-05 ***
TOA_38                         -0.738931   0.208545  -3.543 0.000395 ***
TOA_40                         -1.024697   0.264819  -3.869 0.000109 ***
TOA_42                          0.108076   0.484634   0.223 0.823532    
TOA_44                          0.963188   1.098288   0.877 0.380492    
TOA_45                          8.185772 134.352567   0.061 0.951417    
PPGROUP_11                      0.273923   0.154810   1.769 0.076825 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13915  on 14834  degrees of freedom
AIC: 14087

Number of Fisher Scoring iterations: 10

                                     VIF
GSEGRD                         14.084657
IndAvgSalary                   42.945520
SalaryOverUnderIndAvg           9.658435
LowerLimitAge                   2.404092
BLS_FEDERAL_OtherSep_Rate      13.418547
BLS_FEDERAL_Quits_Rate         38.714968
BLS_FEDERAL_OtherSep_Level     20.402028
BLS_FEDERAL_Quits_Level        55.686488
BLS_FEDERAL_JobOpenings_Level   3.613370
BLS_FEDERAL_Layoffs_Rate       18.822912
BLS_FEDERAL_TotalSep_Rate      44.242917
SALARYLog                     103.348057
LOSSqrt                         1.952779
SEPCount_EFDATE_OCCLog          1.120721
SEPCount_EFDATE_LOCLog         22.379329
IndAvgSalaryLog               142.796845
AGELVL_B                        1.168674
AGELVL_C                        1.669532
AGELVL_E                        1.408634
AGELVL_F                        1.286868
AGELVL_G                        1.270860
AGELVL_H                        1.361018
AGELVL_I                        1.522204
LOC_01                          5.977821
LOC_02                          3.003188
LOC_04                          9.532952
LOC_05                          3.272509
LOC_06                         35.875014
LOC_08                         11.623086
LOC_09                          2.276701
LOC_10                          1.398734
LOC_11                         51.919874
LOC_12                         14.132643
LOC_13                         13.963587
LOC_15                          6.341591
LOC_16                          2.666040
LOC_17                          8.092630
LOC_18                          3.107775
LOC_19                          2.194186
LOC_20                          2.927057
LOC_21                          4.230129
LOC_22                          4.045685
LOC_23                          2.101691
LOC_24                         35.056214
LOC_25                          5.172459
LOC_26                          4.400721
LOC_27                          3.238691
LOC_28                          3.122100
LOC_29                          6.337389
LOC_30                          3.236332
LOC_31                          2.118288
LOC_32                          2.983542
LOC_33                          1.493756
LOC_34                          4.508410
LOC_35                          7.331036
LOC_36                          9.440269
LOC_37                          7.599446
LOC_38                          1.942321
LOC_39                          8.672915
LOC_40                          6.230374
LOC_41                          4.674291
LOC_42                          8.981797
LOC_44                          1.495404
LOC_45                          3.621864
LOC_46                          2.901286
LOC_47                          3.995110
LOC_48                         28.978212
LOC_49                          4.202065
LOC_50                          1.410012
LOC_51                         40.241437
LOC_53                         11.362128
LOC_54                          2.715366
LOC_55                          3.017254
TOA_10                         22.327307
TOA_15                         12.074569
TOA_20                          2.099988
TOA_30                          7.349533
TOA_32                          1.071774
TOA_35                          1.551110
TOA_38                         10.823001
TOA_40                          2.347558
TOA_42                          1.209745
TOA_44                          1.039621
TOA_45                          1.000003
PPGROUP_11                      1.882529



Removed BEFORE this step: IndAvgSalaryLog 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0395  -0.7019  -0.1263   0.7840   3.3497  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     6.906640   0.736087   9.383  < 2e-16 ***
GSEGRD                         -3.596831   0.319373 -11.262  < 2e-16 ***
IndAvgSalary                   -0.350536   0.716317  -0.489 0.624588    
SalaryOverUnderIndAvg          -5.597460   1.183704  -4.729 2.26e-06 ***
LowerLimitAge                  -2.259397   0.124326 -18.173  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       4.289045   0.282966  15.157  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         -8.624422   0.565072 -15.263  < 2e-16 ***
BLS_FEDERAL_OtherSep_Level     -2.723885   0.366385  -7.434 1.05e-13 ***
BLS_FEDERAL_Quits_Level         9.963670   0.616793  16.154  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   2.261407   0.123118  18.368  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        2.099096   0.365336   5.746 9.16e-09 ***
BLS_FEDERAL_TotalSep_Rate      -2.739025   0.567558  -4.826 1.39e-06 ***
SALARYLog                       5.762727   0.974318   5.915 3.33e-09 ***
LOSSqrt                        -6.572608   0.201050 -32.691  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.280155   0.108567  -2.580 0.009867 ** 
SEPCount_EFDATE_LOCLog         -1.706905   0.608355  -2.806 0.005020 ** 
AGELVL_B                       -0.706011   0.229297  -3.079 0.002077 ** 
AGELVL_C                       -0.143565   0.087596  -1.639 0.101226    
AGELVL_E                        0.273363   0.069085   3.957 7.59e-05 ***
AGELVL_F                        0.495350   0.072035   6.877 6.13e-12 ***
AGELVL_G                        0.735430   0.075918   9.687  < 2e-16 ***
AGELVL_H                        0.882430   0.078205  11.284  < 2e-16 ***
AGELVL_I                        0.746257   0.082734   9.020  < 2e-16 ***
LOC_01                         -0.123070   0.405212  -0.304 0.761344    
LOC_02                          0.037446   0.439026   0.085 0.932027    
LOC_04                          0.658076   0.408487   1.611 0.107178    
LOC_05                         -0.047360   0.424826  -0.111 0.911235    
LOC_06                          0.970105   0.477139   2.033 0.042035 *  
LOC_08                          0.386055   0.411947   0.937 0.348683    
LOC_09                         -0.546753   0.483926  -1.130 0.258549    
LOC_10                         -0.569186   0.697556  -0.816 0.414516    
LOC_11                          0.440024   0.462595   0.951 0.341499    
LOC_12                          0.396511   0.423743   0.936 0.349410    
LOC_13                          0.469405   0.414499   1.132 0.257439    
LOC_15                         -0.150323   0.397084  -0.379 0.705010    
LOC_16                         -0.062495   0.448029  -0.139 0.889065    
LOC_17                         -0.098103   0.407690  -0.241 0.809840    
LOC_18                         -0.235380   0.435694  -0.540 0.589030    
LOC_19                         -0.035998   0.480573  -0.075 0.940290    
LOC_20                          0.595410   0.438344   1.358 0.174363    
LOC_21                          0.036742   0.414618   0.089 0.929387    
LOC_22                         -0.206615   0.412208  -0.501 0.616202    
LOC_23                         -0.110210   0.496983  -0.222 0.824502    
LOC_24                          0.325570   0.439503   0.741 0.458834    
LOC_25                          0.137858   0.408894   0.337 0.736005    
LOC_26                         -0.050160   0.414859  -0.121 0.903763    
LOC_27                          0.266200   0.432238   0.616 0.537984    
LOC_28                         -0.107461   0.432771  -0.248 0.803895    
LOC_29                         -0.085410   0.424818  -0.201 0.840659    
LOC_30                          0.591700   0.432164   1.369 0.170951    
LOC_31                         -0.049033   0.485856  -0.101 0.919613    
LOC_32                          0.112455   0.435029   0.258 0.796022    
LOC_33                         -0.248886   0.647291  -0.385 0.700605    
LOC_34                         -0.201738   0.409479  -0.493 0.622246    
LOC_35                          0.524118   0.398362   1.316 0.188281    
LOC_36                          0.259577   0.425193   0.610 0.541536    
LOC_37                          0.306726   0.411426   0.746 0.455957    
LOC_38                          0.198406   0.510653   0.389 0.697620    
LOC_39                         -0.046536   0.408250  -0.114 0.909247    
LOC_40                          0.300275   0.403433   0.744 0.456696    
LOC_41                          0.249117   0.420822   0.592 0.553866    
LOC_42                         -0.024319   0.422154  -0.058 0.954061    
LOC_44                         -0.341985   0.644032  -0.531 0.595415    
LOC_45                          0.011214   0.422106   0.027 0.978806    
LOC_46                          0.591692   0.436343   1.356 0.175092    
LOC_47                         -0.138093   0.426084  -0.324 0.745863    
LOC_48                          0.844191   0.458380   1.842 0.065522 .  
LOC_49                          0.008617   0.423870   0.020 0.983780    
LOC_50                         -0.648089   0.698444  -0.928 0.353458    
LOC_51                          0.279143   0.452567   0.617 0.537366    
LOC_53                          0.671360   0.419378   1.601 0.109410    
LOC_54                         -0.258787   0.445566  -0.581 0.561371    
LOC_55                         -0.388616   0.436938  -0.889 0.373785    
TOA_10                         -1.102226   0.203332  -5.421 5.93e-08 ***
TOA_15                         -1.026563   0.208322  -4.928 8.32e-07 ***
TOA_20                          0.070055   0.274660   0.255 0.798677    
TOA_30                         -0.739151   0.218725  -3.379 0.000727 ***
TOA_32                         -0.452898   0.813465  -0.557 0.577697    
TOA_35                         -1.611277   0.353128  -4.563 5.05e-06 ***
TOA_38                         -0.710028   0.208822  -3.400 0.000673 ***
TOA_40                         -1.064993   0.265202  -4.016 5.92e-05 ***
TOA_42                          0.108791   0.484503   0.225 0.822336    
TOA_44                          0.913620   1.099846   0.831 0.406155    
TOA_45                          8.078026 134.333434   0.060 0.952049    
PPGROUP_11                      0.384060   0.152351   2.521 0.011706 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 13940  on 14835  degrees of freedom
AIC: 14110

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                        10.387833
IndAvgSalary                  31.307265
SalaryOverUnderIndAvg          5.136218
LowerLimitAge                  2.394977
BLS_FEDERAL_OtherSep_Rate     13.403790
BLS_FEDERAL_Quits_Rate        38.696489
BLS_FEDERAL_OtherSep_Level    20.390819
BLS_FEDERAL_Quits_Level       55.669333
BLS_FEDERAL_JobOpenings_Level  3.612427
BLS_FEDERAL_Layoffs_Rate      18.792428
BLS_FEDERAL_TotalSep_Rate     44.170526
SALARYLog                     47.439530
LOSSqrt                        1.933243
SEPCount_EFDATE_OCCLog         1.119350
SEPCount_EFDATE_LOCLog        22.326922
AGELVL_B                       1.170956
AGELVL_C                       1.666558
AGELVL_E                       1.408466
AGELVL_F                       1.286235
AGELVL_G                       1.271210
AGELVL_H                       1.361442
AGELVL_I                       1.523344
LOC_01                         5.952585
LOC_02                         2.962028
LOC_04                         9.449940
LOC_05                         3.261701
LOC_06                        35.284461
LOC_08                        11.610517
LOC_09                         2.242291
LOC_10                         1.404120
LOC_11                        51.198890
LOC_12                        14.028929
LOC_13                        13.999906
LOC_15                         6.267715
LOC_16                         2.666982
LOC_17                         8.026589
LOC_18                         3.097305
LOC_19                         2.186868
LOC_20                         2.931389
LOC_21                         4.233893
LOC_22                         4.012930
LOC_23                         2.079279
LOC_24                        34.534464
LOC_25                         5.089867
LOC_26                         4.343076
LOC_27                         3.214594
LOC_28                         3.118743
LOC_29                         6.347257
LOC_30                         3.257016
LOC_31                         2.115996
LOC_32                         2.959568
LOC_33                         1.479543
LOC_34                         4.446378
LOC_35                         7.270815
LOC_36                         9.326787
LOC_37                         7.551486
LOC_38                         1.943499
LOC_39                         8.604161
LOC_40                         6.211647
LOC_41                         4.641935
LOC_42                         8.929351
LOC_44                         1.492800
LOC_45                         3.606664
LOC_46                         2.914152
LOC_47                         3.976128
LOC_48                        28.823356
LOC_49                         4.162500
LOC_50                         1.403730
LOC_51                        40.191955
LOC_53                        11.266994
LOC_54                         2.711282
LOC_55                         2.996195
TOA_10                        22.434899
TOA_15                        12.162260
TOA_20                         2.107040
TOA_30                         7.345564
TOA_32                         1.071489
TOA_35                         1.556871
TOA_38                        10.854026
TOA_40                         2.345046
TOA_42                         1.210866
TOA_44                         1.039612
TOA_45                         1.000003
PPGROUP_11                     1.811161



Removed BEFORE this step: BLS_FEDERAL_Quits_Level 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9018  -0.7194  -0.1324   0.8061   3.3221  

Coefficients:
                               Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     7.19136    0.72967   9.856  < 2e-16 ***
GSEGRD                         -3.48689    0.31668 -11.011  < 2e-16 ***
IndAvgSalary                   -0.55016    0.70812  -0.777 0.437199    
SalaryOverUnderIndAvg          -5.51971    1.17455  -4.699 2.61e-06 ***
LowerLimitAge                  -2.24526    0.12297 -18.259  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       2.51134    0.25409   9.884  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.01368    0.17901   0.076 0.939079    
BLS_FEDERAL_OtherSep_Level     -3.56348    0.35562 -10.020  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   0.78695    0.08135   9.673  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       -2.05471    0.26183  -7.847 4.24e-15 ***
BLS_FEDERAL_TotalSep_Rate       4.07657    0.38590  10.564  < 2e-16 ***
SALARYLog                       5.69125    0.96490   5.898 3.67e-09 ***
LOSSqrt                        -6.59656    0.19931 -33.097  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.22651    0.10724  -2.112 0.034675 *  
SEPCount_EFDATE_LOCLog         -2.14811    0.59989  -3.581 0.000342 ***
AGELVL_B                       -0.69239    0.22678  -3.053 0.002265 ** 
AGELVL_C                       -0.13129    0.08679  -1.513 0.130359    
AGELVL_E                        0.27629    0.06826   4.047 5.18e-05 ***
AGELVL_F                        0.49653    0.07115   6.978 2.99e-12 ***
AGELVL_G                        0.73240    0.07498   9.768  < 2e-16 ***
AGELVL_H                        0.87465    0.07728  11.318  < 2e-16 ***
AGELVL_I                        0.74305    0.08201   9.060  < 2e-16 ***
LOC_01                         -0.05601    0.39985  -0.140 0.888592    
LOC_02                          0.05742    0.43370   0.132 0.894672    
LOC_04                          0.75915    0.40367   1.881 0.060021 .  
LOC_05                         -0.05827    0.41962  -0.139 0.889549    
LOC_06                          1.18146    0.47122   2.507 0.012167 *  
LOC_08                          0.49869    0.40712   1.225 0.220606    
LOC_09                         -0.58307    0.48245  -1.209 0.226831    
LOC_10                         -0.57326    0.69386  -0.826 0.408690    
LOC_11                          0.66254    0.45646   1.451 0.146646    
LOC_12                          0.52241    0.41839   1.249 0.211797    
LOC_13                          0.55646    0.40927   1.360 0.173940    
LOC_15                         -0.13449    0.39271  -0.342 0.732000    
LOC_16                         -0.09205    0.44342  -0.208 0.835545    
LOC_17                          0.03706    0.40248   0.092 0.926642    
LOC_18                         -0.26204    0.43195  -0.607 0.544081    
LOC_19                         -0.08922    0.47370  -0.188 0.850606    
LOC_20                          0.55942    0.43318   1.291 0.196549    
LOC_21                          0.03146    0.41059   0.077 0.938932    
LOC_22                         -0.17106    0.40739  -0.420 0.674576    
LOC_23                         -0.26136    0.49078  -0.533 0.594349    
LOC_24                          0.48387    0.43373   1.116 0.264584    
LOC_25                          0.13874    0.40465   0.343 0.731702    
LOC_26                         -0.06759    0.41107  -0.164 0.869401    
LOC_27                          0.25407    0.42765   0.594 0.552435    
LOC_28                         -0.08418    0.42875  -0.196 0.844337    
LOC_29                          0.01340    0.41927   0.032 0.974500    
LOC_30                          0.66091    0.42864   1.542 0.123099    
LOC_31                         -0.05048    0.48339  -0.104 0.916821    
LOC_32                          0.07871    0.42826   0.184 0.854173    
LOC_33                         -0.35097    0.63921  -0.549 0.582960    
LOC_34                         -0.22608    0.40536  -0.558 0.577028    
LOC_35                          0.56599    0.39391   1.437 0.150762    
LOC_36                          0.37252    0.41953   0.888 0.374574    
LOC_37                          0.33598    0.40629   0.827 0.408266    
LOC_38                          0.11283    0.50424   0.224 0.822940    
LOC_39                          0.01617    0.40358   0.040 0.968049    
LOC_40                          0.33901    0.39907   0.850 0.395598    
LOC_41                          0.33936    0.41589   0.816 0.414509    
LOC_42                          0.11207    0.41666   0.269 0.787960    
LOC_44                         -0.51369    0.63127  -0.814 0.415793    
LOC_45                          0.02444    0.41833   0.058 0.953414    
LOC_46                          0.54319    0.43288   1.255 0.209539    
LOC_47                         -0.05328    0.42075  -0.127 0.899227    
LOC_48                          0.98280    0.45225   2.173 0.029768 *  
LOC_49                          0.02927    0.41889   0.070 0.944286    
LOC_50                         -0.90524    0.69163  -1.309 0.190582    
LOC_51                          0.45602    0.44655   1.021 0.307159    
LOC_53                          0.77251    0.41427   1.865 0.062218 .  
LOC_54                         -0.28080    0.44073  -0.637 0.524045    
LOC_55                         -0.36536    0.43151  -0.847 0.397159    
TOA_10                         -1.07075    0.19976  -5.360 8.31e-08 ***
TOA_15                         -1.00556    0.20474  -4.911 9.04e-07 ***
TOA_20                          0.14311    0.27249   0.525 0.599441    
TOA_30                         -0.73711    0.21520  -3.425 0.000614 ***
TOA_32                         -0.61333    0.79777  -0.769 0.442008    
TOA_35                         -1.63550    0.34871  -4.690 2.73e-06 ***
TOA_38                         -0.69988    0.20529  -3.409 0.000651 ***
TOA_40                         -1.08068    0.26100  -4.141 3.47e-05 ***
TOA_42                          0.17808    0.48122   0.370 0.711334    
TOA_44                          1.04330    1.09262   0.955 0.339649    
TOA_45                          8.39085  137.21978   0.061 0.951241    
PPGROUP_11                      0.28850    0.15031   1.919 0.054947 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14209  on 14836  degrees of freedom
AIC: 14377

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                        10.424568
IndAvgSalary                  31.216174
SalaryOverUnderIndAvg          5.125810
LowerLimitAge                  2.387892
BLS_FEDERAL_OtherSep_Rate     10.875194
BLS_FEDERAL_Quits_Rate         4.193359
BLS_FEDERAL_OtherSep_Level    19.417270
BLS_FEDERAL_JobOpenings_Level  1.583558
BLS_FEDERAL_Layoffs_Rate       9.650140
BLS_FEDERAL_TotalSep_Rate     20.424399
SALARYLog                     47.466426
LOSSqrt                        1.927259
SEPCount_EFDATE_OCCLog         1.117936
SEPCount_EFDATE_LOCLog        22.153474
AGELVL_B                       1.171917
AGELVL_C                       1.662913
AGELVL_E                       1.408851
AGELVL_F                       1.288061
AGELVL_G                       1.274118
AGELVL_H                       1.364700
AGELVL_I                       1.523490
LOC_01                         6.047920
LOC_02                         2.980308
LOC_04                         9.395239
LOC_05                         3.281445
LOC_06                        35.067245
LOC_08                        11.530687
LOC_09                         2.205900
LOC_10                         1.401010
LOC_11                        50.768507
LOC_12                        13.990995
LOC_13                        13.927554
LOC_15                         6.250716
LOC_16                         2.659772
LOC_17                         8.087933
LOC_18                         3.066479
LOC_19                         2.206823
LOC_20                         2.938253
LOC_21                         4.188049
LOC_22                         4.026488
LOC_23                         2.091270
LOC_24                        34.578086
LOC_25                         5.046513
LOC_26                         4.281943
LOC_27                         3.203895
LOC_28                         3.099333
LOC_29                         6.320596
LOC_30                         3.228871
LOC_31                         2.091229
LOC_32                         3.019173
LOC_33                         1.482226
LOC_34                         4.407396
LOC_35                         7.197027
LOC_36                         9.405915
LOC_37                         7.613092
LOC_38                         1.950080
LOC_39                         8.532976
LOC_40                         6.158756
LOC_41                         4.640127
LOC_42                         9.008812
LOC_44                         1.506730
LOC_45                         3.562722
LOC_46                         2.885825
LOC_47                         3.986924
LOC_48                        28.862024
LOC_49                         4.170763
LOC_50                         1.402052
LOC_51                        40.072217
LOC_53                        11.336759
LOC_54                         2.712683
LOC_55                         3.013910
TOA_10                        22.095151
TOA_15                        11.934189
TOA_20                         2.059844
TOA_30                         7.207750
TOA_32                         1.071480
TOA_35                         1.548892
TOA_38                        10.705362
TOA_40                         2.331226
TOA_42                         1.203877
TOA_44                         1.038708
TOA_45                         1.000003
PPGROUP_11                     1.801222



Removed BEFORE this step: LOC_11 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9028  -0.7186  -0.1333   0.8045   3.3205  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    7.376e+00  7.188e-01  10.262  < 2e-16 ***
GSEGRD                        -3.482e+00  3.166e-01 -10.996  < 2e-16 ***
IndAvgSalary                  -5.623e-01  7.081e-01  -0.794 0.427148    
SalaryOverUnderIndAvg         -5.519e+00  1.175e+00  -4.698 2.62e-06 ***
LowerLimitAge                 -2.250e+00  1.229e-01 -18.303  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      2.532e+00  2.537e-01   9.978  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         1.278e-02  1.790e-01   0.071 0.943098    
BLS_FEDERAL_OtherSep_Level    -3.616e+00  3.538e-01 -10.221  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level  8.173e-01  7.864e-02  10.392  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate      -2.048e+00  2.617e-01  -7.825 5.06e-15 ***
BLS_FEDERAL_TotalSep_Rate      4.058e+00  3.856e-01  10.523  < 2e-16 ***
SALARYLog                      5.719e+00  9.648e-01   5.928 3.07e-09 ***
LOSSqrt                       -6.600e+00  1.993e-01 -33.108  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -2.219e-01  1.072e-01  -2.071 0.038382 *  
SEPCount_EFDATE_LOCLog        -1.610e+00  4.726e-01  -3.406 0.000658 ***
AGELVL_B                      -6.893e-01  2.268e-01  -3.039 0.002372 ** 
AGELVL_C                      -1.320e-01  8.679e-02  -1.521 0.128343    
AGELVL_E                       2.757e-01  6.826e-02   4.039 5.37e-05 ***
AGELVL_F                       4.942e-01  7.112e-02   6.949 3.68e-12 ***
AGELVL_G                       7.318e-01  7.497e-02   9.761  < 2e-16 ***
AGELVL_H                       8.753e-01  7.727e-02  11.328  < 2e-16 ***
AGELVL_I                       7.427e-01  8.201e-02   9.057  < 2e-16 ***
LOC_01                        -5.429e-01  2.186e-01  -2.484 0.012986 *  
LOC_02                        -3.766e-01  3.152e-01  -1.195 0.232105    
LOC_04                         2.316e-01  1.765e-01   1.312 0.189466    
LOC_05                        -4.678e-01  3.118e-01  -1.500 0.133599    
LOC_06                         5.149e-01  1.053e-01   4.891 1.00e-06 ***
LOC_08                        -4.657e-02  1.577e-01  -0.295 0.767768    
LOC_09                        -9.159e-01  4.259e-01  -2.150 0.031536 *  
LOC_10                        -8.526e-01  6.668e-01  -1.279 0.200988    
LOC_12                        -4.949e-02  1.412e-01  -0.350 0.726003    
LOC_13                         8.769e-04  1.456e-01   0.006 0.995194    
LOC_15                        -6.004e-01  2.275e-01  -2.639 0.008317 ** 
LOC_16                        -5.090e-01  3.385e-01  -1.504 0.132691    
LOC_17                        -4.812e-01  1.867e-01  -2.577 0.009977 ** 
LOC_18                        -7.062e-01  3.059e-01  -2.309 0.020943 *  
LOC_19                        -4.538e-01  4.032e-01  -1.126 0.260362    
LOC_20                         1.330e-01  3.194e-01   0.417 0.677038    
LOC_21                        -4.292e-01  2.617e-01  -1.640 0.100952    
LOC_22                        -6.101e-01  2.742e-01  -2.225 0.026071 *  
LOC_23                        -6.030e-01  4.321e-01  -1.396 0.162826    
LOC_24                        -1.295e-01  9.790e-02  -1.323 0.185760    
LOC_25                        -3.378e-01  2.376e-01  -1.422 0.155029    
LOC_26                        -5.353e-01  2.563e-01  -2.089 0.036734 *  
LOC_27                        -1.917e-01  2.988e-01  -0.641 0.521268    
LOC_28                        -5.197e-01  3.074e-01  -1.691 0.090904 .  
LOC_29                        -5.223e-01  1.996e-01  -2.616 0.008895 ** 
LOC_30                         2.088e-01  2.956e-01   0.706 0.479972    
LOC_31                        -4.305e-01  4.074e-01  -1.057 0.290665    
LOC_32                        -3.357e-01  3.206e-01  -1.047 0.295054    
LOC_33                        -6.502e-01  6.070e-01  -1.071 0.284063    
LOC_34                        -6.764e-01  2.621e-01  -2.581 0.009858 ** 
LOC_35                         7.981e-02  2.084e-01   0.383 0.701730    
LOC_36                        -1.877e-01  1.652e-01  -1.136 0.255979    
LOC_37                        -1.861e-01  1.899e-01  -0.980 0.327027    
LOC_38                        -2.361e-01  4.447e-01  -0.531 0.595422    
LOC_39                        -5.073e-01  1.821e-01  -2.786 0.005332 ** 
LOC_40                        -1.477e-01  2.174e-01  -0.679 0.496845    
LOC_41                        -1.571e-01  2.374e-01  -0.662 0.508030    
LOC_42                        -4.407e-01  1.697e-01  -2.597 0.009401 ** 
LOC_44                        -8.120e-01  5.983e-01  -1.357 0.174682    
LOC_45                        -4.226e-01  2.842e-01  -1.487 0.136989    
LOC_46                         1.526e-01  3.404e-01   0.448 0.654044    
LOC_47                        -5.370e-01  2.577e-01  -2.084 0.037174 *  
LOC_48                         3.449e-01  1.066e-01   3.237 0.001210 ** 
LOC_49                        -4.575e-01  2.521e-01  -1.815 0.069516 .  
LOC_50                        -1.188e+00  6.641e-01  -1.789 0.073561 .  
LOC_51                        -1.779e-01  9.329e-02  -1.907 0.056570 .  
LOC_53                         2.146e-01  1.553e-01   1.382 0.166843    
LOC_54                        -6.873e-01  3.415e-01  -2.013 0.044165 *  
LOC_55                        -7.985e-01  3.130e-01  -2.551 0.010744 *  
TOA_10                        -1.074e+00  1.998e-01  -5.374 7.68e-08 ***
TOA_15                        -1.007e+00  2.047e-01  -4.918 8.73e-07 ***
TOA_20                         1.419e-01  2.725e-01   0.521 0.602561    
TOA_30                        -7.353e-01  2.152e-01  -3.417 0.000633 ***
TOA_32                        -6.111e-01  7.974e-01  -0.766 0.443466    
TOA_35                        -1.632e+00  3.487e-01  -4.680 2.87e-06 ***
TOA_38                        -7.054e-01  2.053e-01  -3.436 0.000590 ***
TOA_40                        -1.081e+00  2.610e-01  -4.141 3.45e-05 ***
TOA_42                         1.774e-01  4.812e-01   0.369 0.712308    
TOA_44                         1.048e+00  1.093e+00   0.960 0.337239    
TOA_45                         8.400e+00  1.373e+02   0.061 0.951210    
PPGROUP_11                     2.932e-01  1.503e-01   1.951 0.051042 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14211  on 14837  degrees of freedom
AIC: 14377

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                        10.423477
IndAvgSalary                  31.212140
SalaryOverUnderIndAvg          5.124258
LowerLimitAge                  2.386331
BLS_FEDERAL_OtherSep_Rate     10.848400
BLS_FEDERAL_Quits_Rate         4.197164
BLS_FEDERAL_OtherSep_Level    19.227923
BLS_FEDERAL_JobOpenings_Level  1.480324
BLS_FEDERAL_Layoffs_Rate       9.649270
BLS_FEDERAL_TotalSep_Rate     20.404316
SALARYLog                     47.446166
LOSSqrt                        1.928047
SEPCount_EFDATE_OCCLog         1.116822
SEPCount_EFDATE_LOCLog        13.746535
AGELVL_B                       1.171734
AGELVL_C                       1.662693
AGELVL_E                       1.408708
AGELVL_F                       1.287400
AGELVL_G                       1.274043
AGELVL_H                       1.364665
AGELVL_I                       1.523507
LOC_01                         1.808712
LOC_02                         1.573336
LOC_04                         1.794369
LOC_05                         1.813621
LOC_06                         1.750672
LOC_08                         1.731481
LOC_09                         1.718502
LOC_10                         1.298869
LOC_12                         1.595397
LOC_13                         1.763800
LOC_15                         2.096169
LOC_16                         1.553767
LOC_17                         1.742101
LOC_18                         1.538529
LOC_19                         1.594760
LOC_20                         1.597676
LOC_21                         1.700998
LOC_22                         1.824250
LOC_23                         1.618931
LOC_24                         1.761633
LOC_25                         1.740938
LOC_26                         1.665517
LOC_27                         1.563316
LOC_28                         1.592336
LOC_29                         1.435470
LOC_30                         1.533660
LOC_31                         1.486334
LOC_32                         1.689900
LOC_33                         1.331779
LOC_34                         1.844282
LOC_35                         2.013207
LOC_36                         1.458929
LOC_37                         1.663214
LOC_38                         1.514536
LOC_39                         1.737623
LOC_40                         1.828184
LOC_41                         1.512959
LOC_42                         1.495241
LOC_44                         1.350909
LOC_45                         1.645623
LOC_46                         1.782745
LOC_47                         1.497655
LOC_48                         1.603522
LOC_49                         1.508097
LOC_50                         1.295152
LOC_51                         1.749001
LOC_53                         1.592749
LOC_54                         1.628118
LOC_55                         1.584264
TOA_10                        22.096299
TOA_15                        11.935872
TOA_20                         2.059526
TOA_30                         7.208034
TOA_32                         1.071549
TOA_35                         1.549060
TOA_38                        10.701220
TOA_40                         2.331845
TOA_42                         1.203958
TOA_44                         1.038700
TOA_45                         1.000003
PPGROUP_11                     1.799377



Removed BEFORE this step: SALARYLog 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9122  -0.7204  -0.1343   0.8061   3.2925  

Coefficients:
                               Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     4.65646    0.55598   8.375  < 2e-16 ***
GSEGRD                         -2.36429    0.24643  -9.594  < 2e-16 ***
IndAvgSalary                    3.14382    0.34398   9.139  < 2e-16 ***
SalaryOverUnderIndAvg           0.52504    0.61063   0.860 0.389879    
LowerLimitAge                  -2.21392    0.12265 -18.051  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       2.52102    0.25343   9.948  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.03248    0.17876   0.182 0.855824    
BLS_FEDERAL_OtherSep_Level     -3.61895    0.35354 -10.236  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   0.81909    0.07858  10.423  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       -2.04099    0.26138  -7.808 5.79e-15 ***
BLS_FEDERAL_TotalSep_Rate       4.05023    0.38509  10.518  < 2e-16 ***
LOSSqrt                        -6.50417    0.19820 -32.816  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.21886    0.10706  -2.044 0.040935 *  
SEPCount_EFDATE_LOCLog         -1.58002    0.47465  -3.329 0.000872 ***
AGELVL_B                       -0.76785    0.22509  -3.411 0.000646 ***
AGELVL_C                       -0.16064    0.08665  -1.854 0.063762 .  
AGELVL_E                        0.27331    0.06827   4.004 6.24e-05 ***
AGELVL_F                        0.48704    0.07105   6.855 7.14e-12 ***
AGELVL_G                        0.72674    0.07486   9.708  < 2e-16 ***
AGELVL_H                        0.86575    0.07702  11.240  < 2e-16 ***
AGELVL_I                        0.73543    0.08171   9.000  < 2e-16 ***
LOC_01                         -0.55568    0.21966  -2.530 0.011416 *  
LOC_02                         -0.31078    0.31464  -0.988 0.323285    
LOC_04                          0.26866    0.17651   1.522 0.127996    
LOC_05                         -0.46595    0.31343  -1.487 0.137119    
LOC_06                          0.55987    0.10459   5.353 8.66e-08 ***
LOC_08                         -0.03527    0.15820  -0.223 0.823590    
LOC_09                         -0.86713    0.42667  -2.032 0.042120 *  
LOC_10                         -0.82652    0.67049  -1.233 0.217680    
LOC_12                         -0.05599    0.14172  -0.395 0.692810    
LOC_13                          0.01182    0.14578   0.081 0.935377    
LOC_15                         -0.52823    0.22743  -2.323 0.020202 *  
LOC_16                         -0.51012    0.33943  -1.503 0.132880    
LOC_17                         -0.46999    0.18732  -2.509 0.012105 *  
LOC_18                         -0.72935    0.30704  -2.375 0.017530 *  
LOC_19                         -0.46373    0.40500  -1.145 0.252203    
LOC_20                          0.10178    0.32089   0.317 0.751116    
LOC_21                         -0.48797    0.26305  -1.855 0.063583 .  
LOC_22                         -0.62696    0.27587  -2.273 0.023048 *  
LOC_23                         -0.58729    0.43362  -1.354 0.175613    
LOC_24                         -0.10371    0.09743  -1.064 0.287105    
LOC_25                         -0.28892    0.23792  -1.214 0.224606    
LOC_26                         -0.53394    0.25761  -2.073 0.038204 *  
LOC_27                         -0.16888    0.29971  -0.563 0.573110    
LOC_28                         -0.55423    0.30925  -1.792 0.073106 .  
LOC_29                         -0.54398    0.20049  -2.713 0.006663 ** 
LOC_30                          0.22513    0.29589   0.761 0.446735    
LOC_31                         -0.47292    0.40882  -1.157 0.247348    
LOC_32                         -0.32986    0.32026  -1.030 0.303020    
LOC_33                         -0.67316    0.60670  -1.110 0.267190    
LOC_34                         -0.60217    0.26235  -2.295 0.021717 *  
LOC_35                          0.08758    0.20888   0.419 0.675016    
LOC_36                         -0.17000    0.16548  -1.027 0.304265    
LOC_37                         -0.16784    0.19031  -0.882 0.377821    
LOC_38                         -0.26580    0.44607  -0.596 0.551256    
LOC_39                         -0.51673    0.18258  -2.830 0.004652 ** 
LOC_40                         -0.15854    0.21828  -0.726 0.467640    
LOC_41                         -0.16453    0.23816  -0.691 0.489675    
LOC_42                         -0.43826    0.17033  -2.573 0.010082 *  
LOC_44                         -0.72605    0.59747  -1.215 0.224288    
LOC_45                         -0.43279    0.28555  -1.516 0.129621    
LOC_46                          0.19621    0.34115   0.575 0.565204    
LOC_47                         -0.55333    0.25814  -2.144 0.032070 *  
LOC_48                          0.35319    0.10645   3.318 0.000907 ***
LOC_49                         -0.48276    0.25392  -1.901 0.057268 .  
LOC_50                         -1.22692    0.66671  -1.840 0.065731 .  
LOC_51                         -0.13807    0.09239  -1.494 0.135075    
LOC_53                          0.25338    0.15538   1.631 0.102945    
LOC_54                         -0.70239    0.34301  -2.048 0.040586 *  
LOC_55                         -0.82923    0.31491  -2.633 0.008458 ** 
TOA_10                         -1.03195    0.20052  -5.146 2.66e-07 ***
TOA_15                         -0.98374    0.20567  -4.783 1.73e-06 ***
TOA_20                          0.13855    0.27366   0.506 0.612667    
TOA_30                         -0.74172    0.21579  -3.437 0.000588 ***
TOA_32                         -0.61688    0.80172  -0.769 0.441625    
TOA_35                         -1.73348    0.34892  -4.968 6.76e-07 ***
TOA_38                         -0.64278    0.20593  -3.121 0.001800 ** 
TOA_40                         -1.08868    0.26153  -4.163 3.15e-05 ***
TOA_42                          0.15164    0.48164   0.315 0.752882    
TOA_44                          0.97018    1.09160   0.889 0.374127    
TOA_45                          8.34210  137.28469   0.061 0.951546    
PPGROUP_11                      0.39958    0.14703   2.718 0.006574 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14247  on 14838  degrees of freedom
AIC: 14411

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                         6.354746
IndAvgSalary                   7.387766
SalaryOverUnderIndAvg          1.390114
LowerLimitAge                  2.385147
BLS_FEDERAL_OtherSep_Rate     10.845686
BLS_FEDERAL_Quits_Rate         4.193261
BLS_FEDERAL_OtherSep_Level    19.238975
BLS_FEDERAL_JobOpenings_Level  1.480661
BLS_FEDERAL_Layoffs_Rate       9.659385
BLS_FEDERAL_TotalSep_Rate     20.421006
LOSSqrt                        1.918764
SEPCount_EFDATE_OCCLog         1.115914
SEPCount_EFDATE_LOCLog        13.892472
AGELVL_B                       1.175315
AGELVL_C                       1.657862
AGELVL_E                       1.408735
AGELVL_F                       1.286049
AGELVL_G                       1.273136
AGELVL_H                       1.363132
AGELVL_I                       1.522601
LOC_01                         1.807478
LOC_02                         1.581945
LOC_04                         1.801385
LOC_05                         1.812283
LOC_06                         1.737309
LOC_08                         1.732556
LOC_09                         1.726621
LOC_10                         1.299121
LOC_12                         1.591573
LOC_13                         1.766830
LOC_15                         2.108753
LOC_16                         1.556842
LOC_17                         1.743444
LOC_18                         1.539210
LOC_19                         1.596619
LOC_20                         1.597037
LOC_21                         1.696238
LOC_22                         1.820706
LOC_23                         1.621595
LOC_24                         1.759641
LOC_25                         1.744307
LOC_26                         1.664777
LOC_27                         1.566618
LOC_28                         1.590589
LOC_29                         1.434045
LOC_30                         1.541124
LOC_31                         1.488876
LOC_32                         1.701623
LOC_33                         1.337304
LOC_34                         1.846826
LOC_35                         2.022638
LOC_36                         1.457910
LOC_37                         1.665035
LOC_38                         1.516009
LOC_39                         1.738870
LOC_40                         1.831806
LOC_41                         1.513169
LOC_42                         1.494536
LOC_44                         1.355905
LOC_45                         1.645585
LOC_46                         1.789284
LOC_47                         1.502473
LOC_48                         1.596570
LOC_49                         1.503876
LOC_50                         1.296301
LOC_51                         1.757573
LOC_53                         1.591402
LOC_54                         1.630070
LOC_55                         1.581435
TOA_10                        22.326646
TOA_15                        12.110061
TOA_20                         2.062102
TOA_30                         7.350634
TOA_32                         1.071336
TOA_35                         1.551092
TOA_38                        10.732947
TOA_40                         2.346874
TOA_42                         1.205710
TOA_44                         1.038804
TOA_45                         1.000003
PPGROUP_11                     1.750610



Removed BEFORE this step: TOA_10 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9215  -0.7214  -0.1332   0.8072   3.3048  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     3.694458   0.522808   7.067 1.59e-12 ***
GSEGRD                         -2.370414   0.245201  -9.667  < 2e-16 ***
IndAvgSalary                    3.115622   0.343065   9.082  < 2e-16 ***
SalaryOverUnderIndAvg           0.577617   0.610966   0.945 0.344447    
LowerLimitAge                  -2.195270   0.122426 -17.931  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       2.535275   0.253161  10.014  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.050269   0.178279   0.282 0.777969    
BLS_FEDERAL_OtherSep_Level     -3.635259   0.353058 -10.296  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level   0.813833   0.078493  10.368  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       -2.010986   0.260913  -7.708 1.28e-14 ***
BLS_FEDERAL_TotalSep_Rate       4.020193   0.384445  10.457  < 2e-16 ***
LOSSqrt                        -6.648746   0.196582 -33.822  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.228219   0.106999  -2.133 0.032932 *  
SEPCount_EFDATE_LOCLog         -1.551062   0.475067  -3.265 0.001095 ** 
AGELVL_B                       -0.747726   0.224513  -3.330 0.000867 ***
AGELVL_C                       -0.159663   0.086378  -1.848 0.064542 .  
AGELVL_E                        0.274131   0.068074   4.027 5.65e-05 ***
AGELVL_F                        0.486299   0.070943   6.855 7.14e-12 ***
AGELVL_G                        0.722580   0.074776   9.663  < 2e-16 ***
AGELVL_H                        0.861372   0.077070  11.176  < 2e-16 ***
AGELVL_I                        0.728860   0.081790   8.911  < 2e-16 ***
LOC_01                         -0.565251   0.220167  -2.567 0.010247 *  
LOC_02                         -0.316896   0.314617  -1.007 0.313817    
LOC_04                          0.254198   0.176522   1.440 0.149857    
LOC_05                         -0.477349   0.313799  -1.521 0.128211    
LOC_06                          0.554664   0.104396   5.313 1.08e-07 ***
LOC_08                         -0.048463   0.158200  -0.306 0.759344    
LOC_09                         -0.871807   0.427645  -2.039 0.041488 *  
LOC_10                         -0.823777   0.671064  -1.228 0.219609    
LOC_12                         -0.051091   0.141605  -0.361 0.718248    
LOC_13                          0.009091   0.145708   0.062 0.950251    
LOC_15                         -0.543626   0.227516  -2.389 0.016876 *  
LOC_16                         -0.524680   0.339882  -1.544 0.122659    
LOC_17                         -0.473938   0.186924  -2.535 0.011230 *  
LOC_18                         -0.712956   0.306834  -2.324 0.020147 *  
LOC_19                         -0.429678   0.404023  -1.063 0.287556    
LOC_20                          0.094546   0.321264   0.294 0.768535    
LOC_21                         -0.505141   0.263317  -1.918 0.055063 .  
LOC_22                         -0.621509   0.276350  -2.249 0.024513 *  
LOC_23                         -0.602439   0.434319  -1.387 0.165415    
LOC_24                         -0.124162   0.097339  -1.276 0.202111    
LOC_25                         -0.269068   0.237156  -1.135 0.256559    
LOC_26                         -0.504955   0.256640  -1.968 0.049118 *  
LOC_27                         -0.179354   0.299816  -0.598 0.549698    
LOC_28                         -0.541084   0.308005  -1.757 0.078962 .  
LOC_29                         -0.554135   0.200075  -2.770 0.005612 ** 
LOC_30                          0.215892   0.295658   0.730 0.465262    
LOC_31                         -0.490646   0.408908  -1.200 0.230181    
LOC_32                         -0.323046   0.319512  -1.011 0.311986    
LOC_33                         -0.677052   0.608170  -1.113 0.265596    
LOC_34                         -0.618442   0.262561  -2.355 0.018502 *  
LOC_35                          0.079828   0.208852   0.382 0.702298    
LOC_36                         -0.149305   0.164811  -0.906 0.364981    
LOC_37                         -0.181128   0.190283  -0.952 0.341153    
LOC_38                         -0.280846   0.446558  -0.629 0.529406    
LOC_39                         -0.504147   0.182389  -2.764 0.005708 ** 
LOC_40                         -0.179244   0.218519  -0.820 0.412064    
LOC_41                         -0.167434   0.237921  -0.704 0.481595    
LOC_42                         -0.446410   0.170173  -2.623 0.008709 ** 
LOC_44                         -0.632787   0.586330  -1.079 0.280484    
LOC_45                         -0.424108   0.284776  -1.489 0.136417    
LOC_46                          0.178224   0.341620   0.522 0.601878    
LOC_47                         -0.548463   0.257571  -2.129 0.033224 *  
LOC_48                          0.336010   0.106354   3.159 0.001581 ** 
LOC_49                         -0.483350   0.253397  -1.907 0.056459 .  
LOC_50                         -1.231207   0.667027  -1.846 0.064920 .  
LOC_51                         -0.162382   0.092297  -1.759 0.078519 .  
LOC_53                          0.242413   0.155263   1.561 0.118452    
LOC_54                         -0.701665   0.343790  -2.041 0.041254 *  
LOC_55                         -0.828843   0.314153  -2.638 0.008331 ** 
TOA_15                         -0.002428   0.070294  -0.035 0.972449    
TOA_20                          1.124604   0.193821   5.802 6.54e-09 ***
TOA_30                          0.262215   0.089840   2.919 0.003515 ** 
TOA_32                          0.399308   0.776562   0.514 0.607112    
TOA_35                         -0.772900   0.292264  -2.645 0.008180 ** 
TOA_38                          0.347670   0.068404   5.083 3.72e-07 ***
TOA_40                         -0.106899   0.176538  -0.606 0.544827    
TOA_42                          1.133587   0.441219   2.569 0.010193 *  
TOA_44                          1.972855   1.074197   1.837 0.066271 .  
TOA_45                          9.287715 137.298034   0.068 0.946067    
PPGROUP_11                      0.375379   0.146774   2.558 0.010542 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14278  on 14839  degrees of freedom
AIC: 14440

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                         6.325307
IndAvgSalary                   7.356269
SalaryOverUnderIndAvg          1.389707
LowerLimitAge                  2.380904
BLS_FEDERAL_OtherSep_Rate     10.826082
BLS_FEDERAL_Quits_Rate         4.183464
BLS_FEDERAL_OtherSep_Level    19.198400
BLS_FEDERAL_JobOpenings_Level  1.481026
BLS_FEDERAL_Layoffs_Rate       9.606578
BLS_FEDERAL_TotalSep_Rate     20.328387
LOSSqrt                        1.885675
SEPCount_EFDATE_OCCLog         1.115541
SEPCount_EFDATE_LOCLog        13.944539
AGELVL_B                       1.172784
AGELVL_C                       1.658781
AGELVL_E                       1.407933
AGELVL_F                       1.285416
AGELVL_G                       1.272661
AGELVL_H                       1.362794
AGELVL_I                       1.520713
LOC_01                         1.804571
LOC_02                         1.585242
LOC_04                         1.803346
LOC_05                         1.811844
LOC_06                         1.739463
LOC_08                         1.732912
LOC_09                         1.724140
LOC_10                         1.299397
LOC_12                         1.593953
LOC_13                         1.770420
LOC_15                         2.113054
LOC_16                         1.555436
LOC_17                         1.751175
LOC_18                         1.541328
LOC_19                         1.603914
LOC_20                         1.596656
LOC_21                         1.696337
LOC_22                         1.819867
LOC_23                         1.620731
LOC_24                         1.756401
LOC_25                         1.753849
LOC_26                         1.675057
LOC_27                         1.568157
LOC_28                         1.600069
LOC_29                         1.437365
LOC_30                         1.540415
LOC_31                         1.490014
LOC_32                         1.707584
LOC_33                         1.336069
LOC_34                         1.847719
LOC_35                         2.028201
LOC_36                         1.465620
LOC_37                         1.666503
LOC_38                         1.516084
LOC_39                         1.744169
LOC_40                         1.831185
LOC_41                         1.516018
LOC_42                         1.496738
LOC_44                         1.372866
LOC_45                         1.653945
LOC_46                         1.788853
LOC_47                         1.507251
LOC_48                         1.595040
LOC_49                         1.508619
LOC_50                         1.296781
LOC_51                         1.752218
LOC_53                         1.593111
LOC_54                         1.627931
LOC_55                         1.587431
TOA_15                         1.412211
TOA_20                         1.029348
TOA_30                         1.266604
TOA_32                         1.006039
TOA_35                         1.086640
TOA_38                         1.179225
TOA_40                         1.064876
TOA_42                         1.011671
TOA_44                         1.005267
TOA_45                         1.000001
PPGROUP_11                     1.742598



Removed BEFORE this step: BLS_FEDERAL_TotalSep_Rate 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9353  -0.7302  -0.1356   0.8100   3.2309  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     2.984275   0.517213   5.770 7.93e-09 ***
GSEGRD                         -2.346394   0.244635  -9.591  < 2e-16 ***
IndAvgSalary                    3.071840   0.342108   8.979  < 2e-16 ***
SalaryOverUnderIndAvg           0.682640   0.608427   1.122 0.261873    
LowerLimitAge                  -2.175766   0.121826 -17.860  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       2.784199   0.251290  11.080  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          1.051257   0.150135   7.002 2.52e-12 ***
BLS_FEDERAL_OtherSep_Level     -2.502596   0.334380  -7.484 7.19e-14 ***
BLS_FEDERAL_JobOpenings_Level   0.634178   0.076055   8.338  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        0.454168   0.112247   4.046 5.21e-05 ***
LOSSqrt                        -6.662789   0.195697 -34.047  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.231095   0.106482  -2.170 0.029986 *  
SEPCount_EFDATE_LOCLog         -1.322210   0.473804  -2.791 0.005261 ** 
AGELVL_B                       -0.774487   0.223744  -3.461 0.000537 ***
AGELVL_C                       -0.163822   0.086060  -1.904 0.056965 .  
AGELVL_E                        0.264428   0.067775   3.902 9.56e-05 ***
AGELVL_F                        0.479034   0.070666   6.779 1.21e-11 ***
AGELVL_G                        0.712042   0.074339   9.578  < 2e-16 ***
AGELVL_H                        0.855255   0.076676  11.154  < 2e-16 ***
AGELVL_I                        0.721706   0.081415   8.865  < 2e-16 ***
LOC_01                         -0.489240   0.219228  -2.232 0.025638 *  
LOC_02                         -0.277639   0.314881  -0.882 0.377925    
LOC_04                          0.283885   0.176103   1.612 0.106953    
LOC_05                         -0.401238   0.312744  -1.283 0.199506    
LOC_06                          0.545028   0.104031   5.239 1.61e-07 ***
LOC_08                         -0.002602   0.157715  -0.016 0.986836    
LOC_09                         -0.725292   0.426102  -1.702 0.088726 .  
LOC_10                         -0.629984   0.665720  -0.946 0.343986    
LOC_12                         -0.024200   0.141429  -0.171 0.864136    
LOC_13                          0.030722   0.145069   0.212 0.832282    
LOC_15                         -0.469150   0.227451  -2.063 0.039147 *  
LOC_16                         -0.409127   0.338748  -1.208 0.227138    
LOC_17                         -0.441340   0.186240  -2.370 0.017800 *  
LOC_18                         -0.629332   0.307524  -2.046 0.040713 *  
LOC_19                         -0.321269   0.404876  -0.794 0.427486    
LOC_20                          0.192114   0.320770   0.599 0.549229    
LOC_21                         -0.406029   0.261739  -1.551 0.120836    
LOC_22                         -0.525897   0.275800  -1.907 0.056546 .  
LOC_23                         -0.525263   0.432276  -1.215 0.224324    
LOC_24                         -0.129526   0.096874  -1.337 0.181206    
LOC_25                         -0.202182   0.235732  -0.858 0.391070    
LOC_26                         -0.422689   0.255597  -1.654 0.098182 .  
LOC_27                         -0.105455   0.296448  -0.356 0.722043    
LOC_28                         -0.492659   0.306819  -1.606 0.108340    
LOC_29                         -0.533243   0.198866  -2.681 0.007331 ** 
LOC_30                          0.263849   0.295460   0.893 0.371852    
LOC_31                         -0.462055   0.408215  -1.132 0.257680    
LOC_32                         -0.228959   0.317243  -0.722 0.470470    
LOC_33                         -0.469684   0.602681  -0.779 0.435789    
LOC_34                         -0.575008   0.262084  -2.194 0.028237 *  
LOC_35                          0.157012   0.207851   0.755 0.450005    
LOC_36                         -0.138088   0.163819  -0.843 0.399268    
LOC_37                         -0.147669   0.190071  -0.777 0.437210    
LOC_38                         -0.172364   0.448109  -0.385 0.700499    
LOC_39                         -0.434422   0.181504  -2.393 0.016691 *  
LOC_40                         -0.119572   0.218048  -0.548 0.583434    
LOC_41                         -0.123199   0.237608  -0.518 0.604112    
LOC_42                         -0.401285   0.169369  -2.369 0.017822 *  
LOC_44                         -0.387221   0.585766  -0.661 0.508579    
LOC_45                         -0.329375   0.284299  -1.159 0.246639    
LOC_46                          0.240068   0.340037   0.706 0.480184    
LOC_47                         -0.474020   0.256330  -1.849 0.064421 .  
LOC_48                          0.333802   0.105932   3.151 0.001627 ** 
LOC_49                         -0.432053   0.251723  -1.716 0.086092 .  
LOC_50                         -1.140132   0.671287  -1.698 0.089427 .  
LOC_51                         -0.153333   0.091943  -1.668 0.095375 .  
LOC_53                          0.277413   0.155104   1.789 0.073685 .  
LOC_54                         -0.595032   0.340478  -1.748 0.080527 .  
LOC_55                         -0.758296   0.312730  -2.425 0.015318 *  
TOA_15                         -0.011441   0.070046  -0.163 0.870254    
TOA_20                          1.091068   0.193751   5.631 1.79e-08 ***
TOA_30                          0.257983   0.089308   2.889 0.003869 ** 
TOA_32                          0.304292   0.765330   0.398 0.690928    
TOA_35                         -0.731444   0.292496  -2.501 0.012395 *  
TOA_38                          0.353165   0.068154   5.182 2.20e-07 ***
TOA_40                         -0.109356   0.175780  -0.622 0.533864    
TOA_42                          1.141137   0.440598   2.590 0.009598 ** 
TOA_44                          1.923327   1.075050   1.789 0.073605 .  
TOA_45                          9.297606 139.035983   0.067 0.946684    
PPGROUP_11                      0.364606   0.146343   2.491 0.012722 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14388  on 14840  degrees of freedom
AIC: 14548

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                         6.341945
IndAvgSalary                   7.370123
SalaryOverUnderIndAvg          1.389753
LowerLimitAge                  2.376196
BLS_FEDERAL_OtherSep_Rate     10.825917
BLS_FEDERAL_Quits_Rate         3.044004
BLS_FEDERAL_OtherSep_Level    17.475471
BLS_FEDERAL_JobOpenings_Level  1.407951
BLS_FEDERAL_Layoffs_Rate       1.799169
LOSSqrt                        1.879479
SEPCount_EFDATE_OCCLog         1.115124
SEPCount_EFDATE_LOCLog        13.998472
AGELVL_B                       1.174410
AGELVL_C                       1.657234
AGELVL_E                       1.408689
AGELVL_F                       1.284917
AGELVL_G                       1.273502
AGELVL_H                       1.363176
AGELVL_I                       1.520633
LOC_01                         1.811694
LOC_02                         1.585316
LOC_04                         1.800230
LOC_05                         1.819679
LOC_06                         1.738628
LOC_08                         1.736083
LOC_09                         1.726487
LOC_10                         1.303526
LOC_12                         1.591695
LOC_13                         1.779967
LOC_15                         2.118655
LOC_16                         1.562957
LOC_17                         1.756577
LOC_18                         1.534246
LOC_19                         1.596140
LOC_20                         1.595951
LOC_21                         1.703234
LOC_22                         1.817510
LOC_23                         1.628642
LOC_24                         1.762368
LOC_25                         1.760211
LOC_26                         1.678602
LOC_27                         1.583312
LOC_28                         1.604592
LOC_29                         1.443580
LOC_30                         1.539174
LOC_31                         1.491019
LOC_32                         1.718984
LOC_33                         1.340572
LOC_34                         1.852480
LOC_35                         2.032909
LOC_36                         1.473198
LOC_37                         1.665442
LOC_38                         1.508745
LOC_39                         1.748613
LOC_40                         1.834955
LOC_41                         1.517115
LOC_42                         1.500269
LOC_44                         1.370811
LOC_45                         1.653431
LOC_46                         1.798056
LOC_47                         1.514145
LOC_48                         1.593242
LOC_49                         1.518887
LOC_50                         1.291666
LOC_51                         1.752969
LOC_53                         1.594601
LOC_54                         1.645998
LOC_55                         1.592307
TOA_15                         1.409587
TOA_20                         1.028883
TOA_30                         1.267760
TOA_32                         1.006042
TOA_35                         1.086763
TOA_38                         1.179202
TOA_40                         1.064819
TOA_42                         1.011394
TOA_44                         1.005168
TOA_45                         1.000001
PPGROUP_11                     1.741409



Removed BEFORE this step: BLS_FEDERAL_OtherSep_Level 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9532  -0.7383  -0.1365   0.8198   3.1762  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     3.595112   0.511889   7.023 2.17e-12 ***
GSEGRD                         -2.337790   0.244065  -9.579  < 2e-16 ***
IndAvgSalary                    3.078945   0.341401   9.019  < 2e-16 ***
SalaryOverUnderIndAvg           0.758550   0.607718   1.248 0.211960    
LowerLimitAge                  -2.160927   0.121657 -17.763  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       1.076123   0.103318  10.416  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.202434   0.097661   2.073 0.038190 *  
BLS_FEDERAL_JobOpenings_Level   0.514378   0.074430   6.911 4.82e-12 ***
BLS_FEDERAL_Layoffs_Rate        0.066239   0.099623   0.665 0.506114    
LOSSqrt                        -6.664643   0.195336 -34.119  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.241742   0.106294  -2.274 0.022949 *  
SEPCount_EFDATE_LOCLog         -1.880063   0.470132  -3.999 6.36e-05 ***
AGELVL_B                       -0.764589   0.223487  -3.421 0.000624 ***
AGELVL_C                       -0.165583   0.085805  -1.930 0.053636 .  
AGELVL_E                        0.268587   0.067569   3.975 7.04e-05 ***
AGELVL_F                        0.473682   0.070444   6.724 1.77e-11 ***
AGELVL_G                        0.703081   0.074203   9.475  < 2e-16 ***
AGELVL_H                        0.844084   0.076496  11.034  < 2e-16 ***
AGELVL_I                        0.718075   0.081333   8.829  < 2e-16 ***
LOC_01                         -0.654037   0.218694  -2.991 0.002784 ** 
LOC_02                         -0.487493   0.313482  -1.555 0.119925    
LOC_04                          0.166013   0.175312   0.947 0.343661    
LOC_05                         -0.650812   0.311206  -2.091 0.036505 *  
LOC_06                          0.568098   0.103796   5.473 4.42e-08 ***
LOC_08                         -0.111257   0.157172  -0.708 0.479025    
LOC_09                         -1.049249   0.427077  -2.457 0.014018 *  
LOC_10                         -0.995616   0.661532  -1.505 0.132320    
LOC_12                         -0.094990   0.141158  -0.673 0.500990    
LOC_13                         -0.067918   0.144501  -0.470 0.638345    
LOC_15                         -0.632404   0.226151  -2.796 0.005168 ** 
LOC_16                         -0.630543   0.337349  -1.869 0.061607 .  
LOC_17                         -0.559472   0.185422  -3.017 0.002550 ** 
LOC_18                         -0.812455   0.307228  -2.644 0.008182 ** 
LOC_19                         -0.638518   0.402042  -1.588 0.112244    
LOC_20                         -0.039060   0.319491  -0.122 0.902696    
LOC_21                         -0.590998   0.260944  -2.265 0.023522 *  
LOC_22                         -0.734349   0.274896  -2.671 0.007554 ** 
LOC_23                         -0.850578   0.428500  -1.985 0.047143 *  
LOC_24                         -0.161405   0.096605  -1.671 0.094767 .  
LOC_25                         -0.362513   0.235071  -1.542 0.123038    
LOC_26                         -0.600093   0.254429  -2.359 0.018345 *  
LOC_27                         -0.333910   0.294946  -1.132 0.257591    
LOC_28                         -0.736229   0.305241  -2.412 0.015867 *  
LOC_29                         -0.637128   0.198703  -3.206 0.001344 ** 
LOC_30                          0.078471   0.293967   0.267 0.789516    
LOC_31                         -0.710348   0.406672  -1.747 0.080684 .  
LOC_32                         -0.464140   0.316512  -1.466 0.142534    
LOC_33                         -0.858619   0.595945  -1.441 0.149650    
LOC_34                         -0.772549   0.260366  -2.967 0.003006 ** 
LOC_35                         -0.003262   0.207162  -0.016 0.987437    
LOC_36                         -0.240697   0.163383  -1.473 0.140694    
LOC_37                         -0.269171   0.189560  -1.420 0.155614    
LOC_38                         -0.473327   0.447432  -1.058 0.290112    
LOC_39                         -0.556743   0.180422  -3.086 0.002030 ** 
LOC_40                         -0.274089   0.217609  -1.260 0.207833    
LOC_41                         -0.277735   0.236888  -1.172 0.241023    
LOC_42                         -0.488331   0.168862  -2.892 0.003829 ** 
LOC_44                         -0.741277   0.578666  -1.281 0.200190    
LOC_45                         -0.563443   0.282901  -1.992 0.046409 *  
LOC_46                         -0.039443   0.337607  -0.117 0.906993    
LOC_47                         -0.652355   0.255033  -2.558 0.010530 *  
LOC_48                          0.333184   0.105567   3.156 0.001599 ** 
LOC_49                         -0.590427   0.250595  -2.356 0.018468 *  
LOC_50                         -1.484484   0.673079  -2.206 0.027418 *  
LOC_51                         -0.164908   0.091769  -1.797 0.072338 .  
LOC_53                          0.187839   0.154334   1.217 0.223567    
LOC_54                         -0.810556   0.338764  -2.393 0.016726 *  
LOC_55                         -0.963334   0.311509  -3.092 0.001985 ** 
TOA_15                         -0.007958   0.069833  -0.114 0.909269    
TOA_20                          1.108300   0.193605   5.725 1.04e-08 ***
TOA_30                          0.255428   0.089203   2.863 0.004190 ** 
TOA_32                          0.309713   0.762822   0.406 0.684736    
TOA_35                         -0.741320   0.291936  -2.539 0.011107 *  
TOA_38                          0.360469   0.067962   5.304 1.13e-07 ***
TOA_40                         -0.099225   0.175059  -0.567 0.570842    
TOA_42                          1.153650   0.440113   2.621 0.008761 ** 
TOA_44                          1.954747   1.073832   1.820 0.068706 .  
TOA_45                          9.496913 138.944053   0.068 0.945507    
PPGROUP_11                      0.384477   0.145972   2.634 0.008441 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14445  on 14841  degrees of freedom
AIC: 14603

Number of Fisher Scoring iterations: 10

                                    VIF
GSEGRD                         6.340938
IndAvgSalary                   7.356814
SalaryOverUnderIndAvg          1.391728
LowerLimitAge                  2.376786
BLS_FEDERAL_OtherSep_Rate      1.837442
BLS_FEDERAL_Quits_Rate         1.304298
BLS_FEDERAL_JobOpenings_Level  1.352153
BLS_FEDERAL_Layoffs_Rate       1.426971
LOSSqrt                        1.876778
SEPCount_EFDATE_OCCLog         1.114597
SEPCount_EFDATE_LOCLog        13.850861
AGELVL_B                       1.173733
AGELVL_C                       1.657051
AGELVL_E                       1.408001
AGELVL_F                       1.285024
AGELVL_G                       1.273653
AGELVL_H                       1.364834
AGELVL_I                       1.522927
LOC_01                         1.801446
LOC_02                         1.579994
LOC_04                         1.797805
LOC_05                         1.813437
LOC_06                         1.736764
LOC_08                         1.729533
LOC_09                         1.703796
LOC_10                         1.302073
LOC_12                         1.587882
LOC_13                         1.771815
LOC_15                         2.115854
LOC_16                         1.564891
LOC_17                         1.756185
LOC_18                         1.527946
LOC_19                         1.593072
LOC_20                         1.588630
LOC_21                         1.697363
LOC_22                         1.808539
LOC_23                         1.628683
LOC_24                         1.763482
LOC_25                         1.755846
LOC_26                         1.675467
LOC_27                         1.575193
LOC_28                         1.596194
LOC_29                         1.441176
LOC_30                         1.531651
LOC_31                         1.488323
LOC_32                         1.704091
LOC_33                         1.342222
LOC_34                         1.854575
LOC_35                         2.022229
LOC_36                         1.466953
LOC_37                         1.660334
LOC_38                         1.501093
LOC_39                         1.748446
LOC_40                         1.825332
LOC_41                         1.510419
LOC_42                         1.498880
LOC_44                         1.375688
LOC_45                         1.641557
LOC_46                         1.791183
LOC_47                         1.510186
LOC_48                         1.594814
LOC_49                         1.514824
LOC_50                         1.285312
LOC_51                         1.752417
LOC_53                         1.592484
LOC_54                         1.649127
LOC_55                         1.590688
TOA_15                         1.409930
TOA_20                         1.028661
TOA_30                         1.269052
TOA_32                         1.006115
TOA_35                         1.085718
TOA_38                         1.178584
TOA_40                         1.065235
TOA_42                         1.011066
TOA_44                         1.005141
TOA_45                         1.000001
PPGROUP_11                     1.732739



Removed BEFORE this step: SEPCount_EFDATE_LOCLog 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9404  -0.7343  -0.1383   0.8212   3.1759  

Coefficients:
                                Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     2.124823   0.355099   5.984 2.18e-09 ***
GSEGRD                         -2.359822   0.243922  -9.675  < 2e-16 ***
IndAvgSalary                    3.061308   0.341128   8.974  < 2e-16 ***
SalaryOverUnderIndAvg           0.702324   0.606716   1.158 0.247034    
LowerLimitAge                  -2.140033   0.121381 -17.631  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       1.003748   0.101644   9.875  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.115143   0.095070   1.211 0.225844    
BLS_FEDERAL_JobOpenings_Level   0.619172   0.069753   8.877  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        0.013348   0.098654   0.135 0.892372    
LOSSqrt                        -6.644012   0.195014 -34.069  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.252287   0.106202  -2.376 0.017523 *  
AGELVL_B                       -0.765207   0.223508  -3.424 0.000618 ***
AGELVL_C                       -0.164942   0.085783  -1.923 0.054507 .  
AGELVL_E                        0.270782   0.067518   4.011 6.06e-05 ***
AGELVL_F                        0.475765   0.070385   6.759 1.38e-11 ***
AGELVL_G                        0.704162   0.074139   9.498  < 2e-16 ***
AGELVL_H                        0.843772   0.076429  11.040  < 2e-16 ***
AGELVL_I                        0.715830   0.081281   8.807  < 2e-16 ***
LOC_01                         -0.130744   0.174904  -0.748 0.454749    
LOC_02                          0.218873   0.258589   0.846 0.397322    
LOC_04                          0.538935   0.148290   3.634 0.000279 ***
LOC_05                          0.137352   0.240517   0.571 0.567951    
LOC_06                          0.468680   0.100695   4.654 3.25e-06 ***
LOC_08                          0.209193   0.135031   1.549 0.121329    
LOC_09                          0.013541   0.334881   0.040 0.967746    
LOC_10                          0.247462   0.580483   0.426 0.669886    
LOC_12                          0.128716   0.129409   0.995 0.319910    
LOC_13                          0.215498   0.125697   1.714 0.086450 .  
LOC_15                         -0.028427   0.168390  -0.169 0.865939    
LOC_16                          0.126356   0.278254   0.454 0.649756    
LOC_17                         -0.140125   0.152573  -0.918 0.358403    
LOC_18                         -0.142750   0.257123  -0.555 0.578769    
LOC_19                          0.302538   0.326873   0.926 0.354679    
LOC_20                          0.687680   0.262551   2.619 0.008813 ** 
LOC_21                          0.020257   0.211164   0.096 0.923577    
LOC_22                         -0.049423   0.214692  -0.230 0.817933    
LOC_23                          0.177401   0.342326   0.518 0.604303    
LOC_24                         -0.067577   0.093649  -0.722 0.470546    
LOC_25                          0.204728   0.186956   1.095 0.273489    
LOC_26                         -0.011078   0.207011  -0.054 0.957324    
LOC_27                          0.325721   0.244579   1.332 0.182939    
LOC_28                         -0.041107   0.250874  -0.164 0.869845    
LOC_29                         -0.289149   0.178074  -1.624 0.104428    
LOC_30                          0.712458   0.248293   2.869 0.004112 ** 
LOC_31                          0.177196   0.340087   0.521 0.602346    
LOC_32                          0.301824   0.251971   1.198 0.230974    
LOC_33                          0.309496   0.520907   0.594 0.552413    
LOC_34                         -0.112547   0.200842  -0.560 0.575224    
LOC_35                          0.515836   0.161541   3.193 0.001407 ** 
LOC_36                          0.031424   0.148378   0.212 0.832277    
LOC_37                          0.130251   0.160822   0.810 0.417993    
LOC_38                          0.519649   0.373061   1.393 0.163639    
LOC_39                         -0.162385   0.150784  -1.077 0.281509    
LOC_40                          0.248290   0.173844   1.428 0.153223    
LOC_41                          0.202300   0.203669   0.993 0.320573    
LOC_42                         -0.190719   0.151309  -1.260 0.207503    
LOC_44                          0.437297   0.497311   0.879 0.379227    
LOC_45                          0.089703   0.230373   0.389 0.696994    
LOC_46                          0.807348   0.263085   3.069 0.002149 ** 
LOC_47                         -0.122362   0.217310  -0.563 0.573384    
LOC_48                          0.328695   0.105469   3.117 0.001830 ** 
LOC_49                         -0.070609   0.214462  -0.329 0.741976    
LOC_50                         -0.251855   0.597202  -0.422 0.673226    
LOC_51                         -0.145979   0.091644  -1.593 0.111186    
LOC_53                          0.462916   0.137883   3.357 0.000787 ***
LOC_54                         -0.001260   0.271717  -0.005 0.996301    
LOC_55                         -0.256819   0.256457  -1.001 0.316627    
TOA_15                         -0.009509   0.069808  -0.136 0.891646    
TOA_20                          1.113546   0.193452   5.756 8.60e-09 ***
TOA_30                          0.253069   0.089182   2.838 0.004544 ** 
TOA_32                          0.336853   0.759307   0.444 0.657309    
TOA_35                         -0.756342   0.291613  -2.594 0.009496 ** 
TOA_38                          0.357627   0.067886   5.268 1.38e-07 ***
TOA_40                         -0.106192   0.175002  -0.607 0.543981    
TOA_42                          1.145346   0.440010   2.603 0.009241 ** 
TOA_44                          1.926942   1.073367   1.795 0.072617 .  
TOA_45                          9.464808 139.004006   0.068 0.945714    
PPGROUP_11                      0.373755   0.145738   2.565 0.010330 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14461  on 14842  degrees of freedom
AIC: 14617

Number of Fisher Scoring iterations: 10

                                   VIF
GSEGRD                        6.336016
IndAvgSalary                  7.358771
SalaryOverUnderIndAvg         1.389651
LowerLimitAge                 2.369312
BLS_FEDERAL_OtherSep_Rate     1.782477
BLS_FEDERAL_Quits_Rate        1.239868
BLS_FEDERAL_JobOpenings_Level 1.188769
BLS_FEDERAL_Layoffs_Rate      1.402466
LOSSqrt                       1.872374
SEPCount_EFDATE_OCCLog        1.114156
AGELVL_B                      1.174018
AGELVL_C                      1.657118
AGELVL_E                      1.408279
AGELVL_F                      1.284967
AGELVL_G                      1.273409
AGELVL_H                      1.363993
AGELVL_I                      1.521333
LOC_01                        1.155460
LOC_02                        1.076294
LOC_04                        1.288291
LOC_05                        1.084919
LOC_06                        1.638853
LOC_08                        1.279200
LOC_09                        1.043171
LOC_10                        1.015127
LOC_12                        1.338156
LOC_13                        1.344786
LOC_15                        1.170698
LOC_16                        1.069523
LOC_17                        1.192634
LOC_18                        1.072685
LOC_19                        1.046542
LOC_20                        1.074673
LOC_21                        1.113178
LOC_22                        1.104323
LOC_23                        1.041955
LOC_24                        1.658068
LOC_25                        1.115819
LOC_26                        1.112352
LOC_27                        1.080944
LOC_28                        1.077166
LOC_29                        1.163211
LOC_30                        1.089820
LOC_31                        1.044267
LOC_32                        1.078497
LOC_33                        1.019745
LOC_34                        1.107036
LOC_35                        1.228142
LOC_36                        1.211008
LOC_37                        1.197596
LOC_38                        1.038371
LOC_39                        1.225100
LOC_40                        1.165699
LOC_41                        1.121614
LOC_42                        1.206579
LOC_44                        1.018926
LOC_45                        1.092520
LOC_46                        1.085858
LOC_47                        1.101845
LOC_48                        1.596293
LOC_49                        1.105336
LOC_50                        1.015236
LOC_51                        1.746972
LOC_53                        1.275738
LOC_54                        1.059452
LOC_55                        1.076786
TOA_15                        1.409602
TOA_20                        1.028455
TOA_30                        1.268027
TOA_32                        1.006043
TOA_35                        1.086146
TOA_38                        1.178145
TOA_40                        1.064906
TOA_42                        1.011026
TOA_44                        1.005096
TOA_45                        1.000001
PPGROUP_11                    1.734680



Removed BEFORE this step: IndAvgSalary 

Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8257  -0.7389  -0.1369   0.8192   3.2349  

Coefficients:
                               Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     2.78301    0.34185   8.141 3.92e-16 ***
GSEGRD                         -0.50450    0.12346  -4.086 4.38e-05 ***
SalaryOverUnderIndAvg           0.21836    0.58786   0.371 0.710304    
LowerLimitAge                  -2.02152    0.11988 -16.863  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       0.97224    0.10128   9.600  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.14216    0.09474   1.500 0.133506    
BLS_FEDERAL_JobOpenings_Level   0.61448    0.06951   8.841  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        0.03408    0.09841   0.346 0.729124    
LOSSqrt                        -6.61610    0.19392 -34.117  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.20291    0.10576  -1.919 0.055040 .  
AGELVL_B                       -0.58650    0.22001  -2.666 0.007681 ** 
AGELVL_C                       -0.16850    0.08534  -1.974 0.048329 *  
AGELVL_E                        0.27405    0.06732   4.071 4.69e-05 ***
AGELVL_F                        0.48937    0.07015   6.976 3.04e-12 ***
AGELVL_G                        0.71219    0.07401   9.623  < 2e-16 ***
AGELVL_H                        0.84666    0.07623  11.106  < 2e-16 ***
AGELVL_I                        0.71755    0.08111   8.847  < 2e-16 ***
LOC_01                         -0.20785    0.17455  -1.191 0.233732    
LOC_02                          0.15631    0.25597   0.611 0.541422    
LOC_04                          0.52889    0.14713   3.595 0.000325 ***
LOC_05                          0.08352    0.23836   0.350 0.726039    
LOC_06                          0.41228    0.10066   4.096 4.21e-05 ***
LOC_08                          0.14565    0.13468   1.081 0.279502    
LOC_09                         -0.05574    0.33263  -0.168 0.866909    
LOC_10                          0.19844    0.58334   0.340 0.733717    
LOC_12                          0.03653    0.12915   0.283 0.777292    
LOC_13                          0.17130    0.12566   1.363 0.172839    
LOC_15                         -0.06696    0.16792  -0.399 0.690072    
LOC_16                          0.05430    0.27539   0.197 0.843696    
LOC_17                         -0.21994    0.15172  -1.450 0.147157    
LOC_18                         -0.22605    0.25531  -0.885 0.375953    
LOC_19                          0.21731    0.32316   0.672 0.501296    
LOC_20                          0.61528    0.26072   2.360 0.018276 *  
LOC_21                         -0.02959    0.20902  -0.142 0.887417    
LOC_22                         -0.10983    0.21320  -0.515 0.606432    
LOC_23                          0.07089    0.34025   0.208 0.834951    
LOC_24                         -0.10774    0.09422  -1.144 0.252826    
LOC_25                          0.14535    0.18686   0.778 0.436632    
LOC_26                         -0.07013    0.20654  -0.340 0.734203    
LOC_27                          0.28983    0.24242   1.196 0.231868    
LOC_28                         -0.12339    0.25063  -0.492 0.622505    
LOC_29                         -0.34854    0.17625  -1.978 0.047985 *  
LOC_30                          0.68207    0.24529   2.781 0.005425 ** 
LOC_31                          0.13445    0.33813   0.398 0.690894    
LOC_32                          0.23324    0.24849   0.939 0.347934    
LOC_33                          0.19923    0.52057   0.383 0.701933    
LOC_34                         -0.18069    0.20030  -0.902 0.367024    
LOC_35                          0.47409    0.15975   2.968 0.003000 ** 
LOC_36                         -0.02760    0.14833  -0.186 0.852409    
LOC_37                          0.06922    0.16004   0.433 0.665374    
LOC_38                          0.49034    0.36976   1.326 0.184807    
LOC_39                         -0.28922    0.15044  -1.922 0.054549 .  
LOC_40                          0.17951    0.17256   1.040 0.298214    
LOC_41                          0.11693    0.20266   0.577 0.563965    
LOC_42                         -0.29356    0.15076  -1.947 0.051510 .  
LOC_44                          0.33995    0.49358   0.689 0.490990    
LOC_45                          0.06030    0.22972   0.263 0.792925    
LOC_46                          0.81583    0.25905   3.149 0.001636 ** 
LOC_47                         -0.17752    0.21533  -0.824 0.409699    
LOC_48                          0.25293    0.10519   2.404 0.016200 *  
LOC_49                         -0.18341    0.21404  -0.857 0.391511    
LOC_50                         -0.40764    0.59472  -0.685 0.493070    
LOC_51                         -0.15406    0.09176  -1.679 0.093158 .  
LOC_53                          0.39933    0.13728   2.909 0.003627 ** 
LOC_54                         -0.06049    0.27042  -0.224 0.823000    
LOC_55                         -0.31968    0.25484  -1.254 0.209681    
TOA_15                          0.06868    0.06912   0.994 0.320443    
TOA_20                          1.12794    0.19280   5.850 4.91e-09 ***
TOA_30                          0.34640    0.08928   3.880 0.000104 ***
TOA_32                          0.42030    0.75397   0.557 0.577220    
TOA_35                         -0.53484    0.28671  -1.865 0.062116 .  
TOA_38                          0.32873    0.06730   4.884 1.04e-06 ***
TOA_40                         -0.10541    0.17648  -0.597 0.550292    
TOA_42                          1.14078    0.44139   2.585 0.009751 ** 
TOA_44                          2.16479    1.07265   2.018 0.043574 *  
TOA_45                          9.28780  139.01282   0.067 0.946731    
PPGROUP_11                     -0.40956    0.11723  -3.494 0.000477 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14543  on 14843  degrees of freedom
AIC: 14697

Number of Fisher Scoring iterations: 10

                                   VIF
GSEGRD                        1.709771
SalaryOverUnderIndAvg         1.362063
LowerLimitAge                 2.331432
BLS_FEDERAL_OtherSep_Rate     1.780588
BLS_FEDERAL_Quits_Rate        1.239370
BLS_FEDERAL_JobOpenings_Level 1.189501
BLS_FEDERAL_Layoffs_Rate      1.400736
LOSSqrt                       1.863535
SEPCount_EFDATE_OCCLog        1.110247
AGELVL_B                      1.168836
AGELVL_C                      1.661736
AGELVL_E                      1.407665
AGELVL_F                      1.284149
AGELVL_G                      1.271136
AGELVL_H                      1.360827
AGELVL_I                      1.516939
LOC_01                        1.155470
LOC_02                        1.077857
LOC_04                        1.298072
LOC_05                        1.086904
LOC_06                        1.643628
LOC_08                        1.281373
LOC_09                        1.043573
LOC_10                        1.015121
LOC_12                        1.333413
LOC_13                        1.347233
LOC_15                        1.173350
LOC_16                        1.070565
LOC_17                        1.194540
LOC_18                        1.073599
LOC_19                        1.046991
LOC_20                        1.074966
LOC_21                        1.115080
LOC_22                        1.105564
LOC_23                        1.041794
LOC_24                        1.659934
LOC_25                        1.116609
LOC_26                        1.113438
LOC_27                        1.083260
LOC_28                        1.076607
LOC_29                        1.166602
LOC_30                        1.092269
LOC_31                        1.044699
LOC_32                        1.080552
LOC_33                        1.019291
LOC_34                        1.108298
LOC_35                        1.236012
LOC_36                        1.212675
LOC_37                        1.199467
LOC_38                        1.038974
LOC_39                        1.218402
LOC_40                        1.167278
LOC_41                        1.121708
LOC_42                        1.204208
LOC_44                        1.018967
LOC_45                        1.093473
LOC_46                        1.088792
LOC_47                        1.104474
LOC_48                        1.598139
LOC_49                        1.103074
LOC_50                        1.014634
LOC_51                        1.786297
LOC_53                        1.278948
LOC_54                        1.060266
LOC_55                        1.077488
TOA_15                        1.405927
TOA_20                        1.028658
TOA_30                        1.249601
TOA_32                        1.005946
TOA_35                        1.078285
TOA_38                        1.177160
TOA_40                        1.063196
TOA_42                        1.010740
TOA_44                        1.004523
TOA_45                        1.000001
PPGROUP_11                    1.158838

Following variables removed based on VIF values (in order of removal):
 [1] "BLS_FEDERAL_JobOpenings_Rate" "BLS_FEDERAL_TotalSep_Level"  
 [3] "BLS_FEDERAL_Layoffs_Level"    "AGELVL_D"                    
 [5] "IndAvgSalaryLog"              "BLS_FEDERAL_Quits_Level"     
 [7] "LOC_11"                       "SALARYLog"                   
 [9] "TOA_10"                       "BLS_FEDERAL_TotalSep_Rate"   
[11] "BLS_FEDERAL_OtherSep_Level"   "SEPCount_EFDATE_LOCLog"      
[13] "IndAvgSalary"                


Null Deviances (in order):
 [1] "7226.05152246447" "7219.36370064358" "6767.88407188207" "6767.82808686826"
 [5] "6742.92826531162" "6473.82478843413" "6471.71501030948" "6436.43729999307"
 [9] "6405.40525464847" "6294.74008501623" "6238.61063516369" "6222.45281211915"
[13] "6140.09618413702"

Min value at iteration =  13

Diff Degrees of Freedom (in order):
 [1] "88" "87" "86" "85" "84" "83" "82" "81" "80" "79" "78" "77" "76"

Min value at iteration =  13

Log Likelihoods (in order):
 [1] "-6728.54665990542" "-6731.89057081586" "-6957.63038519662"
 [4] "-6957.65837770352" "-6970.10828848184" "-7104.66002692059"
 [7] "-7105.71491598291" "-7123.35377114112" "-7138.86979381342"
[10] "-7194.20237862954" "-7222.2671035558"  "-7230.34601507808"
[13] "-7271.52432906914"

Min value at iteration =  13

AIC values (in order):
 [1] "13635.0933198108" "13639.7811416317" "14089.2607703932" "14087.316755407" 
 [5] "14110.2165769637" "14377.3200538412" "14377.4298319658" "14410.7075422822"
 [9] "14439.7395876268" "14548.4047572591" "14602.5342071116" "14616.6920301562"
[13] "14697.0486581383"

Min value at iteration =  1

BIC values (in order):
 [1] "14312.4240705753" "14309.5014345224" "14751.3706054102" "14741.8161325502"
 [5] "14757.1054962331" "15016.5985152368" "15009.0978354877" "15034.7650879304"
 [9] "15056.1866754012" "15157.2413871597" "15203.7603791385" "15210.3077443093"
[13] "15283.0539144176"

Min value at iteration =  2
In [139]:
%%R
#vars.Repeat <- replace(vars.Repeat, vars.Repeat == "IndAvgSalary", "IndAvgSalaryLog")
vars.Repeat <- vars.Repeat[!vars.Repeat %in% c("IndAvgSalary", "IndAvgSalaryLog")]
In [140]:
%%R

##data.frame(summary(BinLogit)$coef[summary(BinLogit)$coef[,4] <= .05, 4]) #Review coefficients of p-value less than 0.05
#LogitCoeffs <- data.frame(summary(BinLogit.P.Repeat)$coef[-1,4]) #Ignore Intercept and only look at p-values
##LogitCoeffs[LogitCoeffs$`summary.BinLogit..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit..coef..1..4.`),]
#maxP <- cbind(rownames(LogitCoeffs)[LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.`)],
#      max(LogitCoeffs))
#maxP[1,1]

varsP.Repeat <- vars.Repeat

P.removed <- vector(mode="character", length=0)
ndev.vect <- vector(mode="character", length=0)
ndf.vect <- vector(mode="character", length=0)
pchisq.vect <- vector(mode="character", length=0)
logLik.vect <- vector(mode="character", length=0)
AIC.vect <- vector(mode="character", length=0)
BIC.vect <- vector(mode="character", length=0)

for(i in seq(1,44)){
    BinLogit.P.Repeat <- runLogit(remove, varsP.Repeat)
    print(summary(BinLogit.P.Repeat))
    
    LogitCoeffs <- data.frame(summary(BinLogit.P.Repeat)$coef[-1,4]) #Ignore Intercept and only look at p-values
    maxP <- cbind(rownames(LogitCoeffs)[LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.`)],
          max(LogitCoeffs))
    
    vifs.BinLogitP.Repeat <- runVifs(BinLogit.P.Repeat)
    
    remove <- maxP[1,1]
    #remove <- ifelse(grepl('[[:digit:]]$', remove), substr(remove, 1, nchar(remove)-1), remove)
    P.removed <- c(P.removed, remove)
    cat("\nRemoved AFTER this step:", remove, "\n\n\n")
    varsP.Repeat <- varsP.Repeat[!(varsP.Repeat %in% c(remove))]
    
    ##goodness of fit
    ndev.vect <- c(ndev.vect, with(BinLogit.P.Repeat, null.deviance - deviance))
    ndf.vect <- c(ndf.vect, with(BinLogit.P.Repeat, df.null - df.residual))
    pchisq.vect <- c(pchisq.vect, with(BinLogit.P.Repeat, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE)))
    logLik.vect <- c(logLik.vect, logLik(BinLogit.P.Repeat))
    AIC.vect <- c(AIC.vect, AIC(BinLogit.P.Repeat))
    BIC.vect <- c(BIC.vect, BIC(BinLogit.P.Repeat))
}

cat("\nFollowing variables removed based on p-values (in order of removal):\n")
print(P.removed)

cat("\n\nNull Deviances (in order):\n")
print(ndev.vect)
cat("\nMin value at iteration = ", which.min(ndev.vect))

cat("\n\nDiff Degrees of Freedom (in order):\n")
print(ndf.vect)
cat("\nMin value at iteration = ", which.min(ndf.vect))

#cat("\n\nP-ChiSquare (in order):\n")
#print(pchisq.vect)
#cat("\nMin value at iteration = ", which.min(pchisq.vect))

cat("\n\nLog Likelihoods (in order):\n")
print(logLik.vect)
cat("\nMin value at iteration = ", which.min(logLik.vect))

cat("\n\nAIC values (in order):\n")
print(AIC.vect)
cat("\nMin value at iteration = ", which.min(AIC.vect))

cat("\n\nBIC values (in order):\n")
print(BIC.vect)
cat("\nMin value at iteration = ", which.min(BIC.vect))
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8257  -0.7389  -0.1369   0.8192   3.2349  

Coefficients:
                               Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     2.78301    0.34185   8.141 3.92e-16 ***
GSEGRD                         -0.50450    0.12346  -4.086 4.38e-05 ***
SalaryOverUnderIndAvg           0.21836    0.58786   0.371 0.710304    
LowerLimitAge                  -2.02152    0.11988 -16.863  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate       0.97224    0.10128   9.600  < 2e-16 ***
BLS_FEDERAL_Quits_Rate          0.14216    0.09474   1.500 0.133506    
BLS_FEDERAL_JobOpenings_Level   0.61448    0.06951   8.841  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate        0.03408    0.09841   0.346 0.729124    
LOSSqrt                        -6.61610    0.19392 -34.117  < 2e-16 ***
SEPCount_EFDATE_OCCLog         -0.20291    0.10576  -1.919 0.055040 .  
AGELVL_B                       -0.58650    0.22001  -2.666 0.007681 ** 
AGELVL_C                       -0.16850    0.08534  -1.974 0.048329 *  
AGELVL_E                        0.27405    0.06732   4.071 4.69e-05 ***
AGELVL_F                        0.48937    0.07015   6.976 3.04e-12 ***
AGELVL_G                        0.71219    0.07401   9.623  < 2e-16 ***
AGELVL_H                        0.84666    0.07623  11.106  < 2e-16 ***
AGELVL_I                        0.71755    0.08111   8.847  < 2e-16 ***
LOC_01                         -0.20785    0.17455  -1.191 0.233732    
LOC_02                          0.15631    0.25597   0.611 0.541422    
LOC_04                          0.52889    0.14713   3.595 0.000325 ***
LOC_05                          0.08352    0.23836   0.350 0.726039    
LOC_06                          0.41228    0.10066   4.096 4.21e-05 ***
LOC_08                          0.14565    0.13468   1.081 0.279502    
LOC_09                         -0.05574    0.33263  -0.168 0.866909    
LOC_10                          0.19844    0.58334   0.340 0.733717    
LOC_12                          0.03653    0.12915   0.283 0.777292    
LOC_13                          0.17130    0.12566   1.363 0.172839    
LOC_15                         -0.06696    0.16792  -0.399 0.690072    
LOC_16                          0.05430    0.27539   0.197 0.843696    
LOC_17                         -0.21994    0.15172  -1.450 0.147157    
LOC_18                         -0.22605    0.25531  -0.885 0.375953    
LOC_19                          0.21731    0.32316   0.672 0.501296    
LOC_20                          0.61528    0.26072   2.360 0.018276 *  
LOC_21                         -0.02959    0.20902  -0.142 0.887417    
LOC_22                         -0.10983    0.21320  -0.515 0.606432    
LOC_23                          0.07089    0.34025   0.208 0.834951    
LOC_24                         -0.10774    0.09422  -1.144 0.252826    
LOC_25                          0.14535    0.18686   0.778 0.436632    
LOC_26                         -0.07013    0.20654  -0.340 0.734203    
LOC_27                          0.28983    0.24242   1.196 0.231868    
LOC_28                         -0.12339    0.25063  -0.492 0.622505    
LOC_29                         -0.34854    0.17625  -1.978 0.047985 *  
LOC_30                          0.68207    0.24529   2.781 0.005425 ** 
LOC_31                          0.13445    0.33813   0.398 0.690894    
LOC_32                          0.23324    0.24849   0.939 0.347934    
LOC_33                          0.19923    0.52057   0.383 0.701933    
LOC_34                         -0.18069    0.20030  -0.902 0.367024    
LOC_35                          0.47409    0.15975   2.968 0.003000 ** 
LOC_36                         -0.02760    0.14833  -0.186 0.852409    
LOC_37                          0.06922    0.16004   0.433 0.665374    
LOC_38                          0.49034    0.36976   1.326 0.184807    
LOC_39                         -0.28922    0.15044  -1.922 0.054549 .  
LOC_40                          0.17951    0.17256   1.040 0.298214    
LOC_41                          0.11693    0.20266   0.577 0.563965    
LOC_42                         -0.29356    0.15076  -1.947 0.051510 .  
LOC_44                          0.33995    0.49358   0.689 0.490990    
LOC_45                          0.06030    0.22972   0.263 0.792925    
LOC_46                          0.81583    0.25905   3.149 0.001636 ** 
LOC_47                         -0.17752    0.21533  -0.824 0.409699    
LOC_48                          0.25293    0.10519   2.404 0.016200 *  
LOC_49                         -0.18341    0.21404  -0.857 0.391511    
LOC_50                         -0.40764    0.59472  -0.685 0.493070    
LOC_51                         -0.15406    0.09176  -1.679 0.093158 .  
LOC_53                          0.39933    0.13728   2.909 0.003627 ** 
LOC_54                         -0.06049    0.27042  -0.224 0.823000    
LOC_55                         -0.31968    0.25484  -1.254 0.209681    
TOA_15                          0.06868    0.06912   0.994 0.320443    
TOA_20                          1.12794    0.19280   5.850 4.91e-09 ***
TOA_30                          0.34640    0.08928   3.880 0.000104 ***
TOA_32                          0.42030    0.75397   0.557 0.577220    
TOA_35                         -0.53484    0.28671  -1.865 0.062116 .  
TOA_38                          0.32873    0.06730   4.884 1.04e-06 ***
TOA_40                         -0.10541    0.17648  -0.597 0.550292    
TOA_42                          1.14078    0.44139   2.585 0.009751 ** 
TOA_44                          2.16479    1.07265   2.018 0.043574 *  
TOA_45                          9.28780  139.01282   0.067 0.946731    
PPGROUP_11                     -0.40956    0.11723  -3.494 0.000477 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14543  on 14843  degrees of freedom
AIC: 14697

Number of Fisher Scoring iterations: 10

                                   VIF
GSEGRD                        1.709771
SalaryOverUnderIndAvg         1.362063
LowerLimitAge                 2.331432
BLS_FEDERAL_OtherSep_Rate     1.780588
BLS_FEDERAL_Quits_Rate        1.239370
BLS_FEDERAL_JobOpenings_Level 1.189501
BLS_FEDERAL_Layoffs_Rate      1.400736
LOSSqrt                       1.863535
SEPCount_EFDATE_OCCLog        1.110247
AGELVL_B                      1.168836
AGELVL_C                      1.661736
AGELVL_E                      1.407665
AGELVL_F                      1.284149
AGELVL_G                      1.271136
AGELVL_H                      1.360827
AGELVL_I                      1.516939
LOC_01                        1.155470
LOC_02                        1.077857
LOC_04                        1.298072
LOC_05                        1.086904
LOC_06                        1.643628
LOC_08                        1.281373
LOC_09                        1.043573
LOC_10                        1.015121
LOC_12                        1.333413
LOC_13                        1.347233
LOC_15                        1.173350
LOC_16                        1.070565
LOC_17                        1.194540
LOC_18                        1.073599
LOC_19                        1.046991
LOC_20                        1.074966
LOC_21                        1.115080
LOC_22                        1.105564
LOC_23                        1.041794
LOC_24                        1.659934
LOC_25                        1.116609
LOC_26                        1.113438
LOC_27                        1.083260
LOC_28                        1.076607
LOC_29                        1.166602
LOC_30                        1.092269
LOC_31                        1.044699
LOC_32                        1.080552
LOC_33                        1.019291
LOC_34                        1.108298
LOC_35                        1.236012
LOC_36                        1.212675
LOC_37                        1.199467
LOC_38                        1.038974
LOC_39                        1.218402
LOC_40                        1.167278
LOC_41                        1.121708
LOC_42                        1.204208
LOC_44                        1.018967
LOC_45                        1.093473
LOC_46                        1.088792
LOC_47                        1.104474
LOC_48                        1.598139
LOC_49                        1.103074
LOC_50                        1.014634
LOC_51                        1.786297
LOC_53                        1.278948
LOC_54                        1.060266
LOC_55                        1.077488
TOA_15                        1.405927
TOA_20                        1.028658
TOA_30                        1.249601
TOA_32                        1.005946
TOA_35                        1.078285
TOA_38                        1.177160
TOA_40                        1.063196
TOA_42                        1.010740
TOA_44                        1.004523
TOA_45                        1.000001
PPGROUP_11                    1.158838

Removed AFTER this step: TOA_45 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8259  -0.7389  -0.1369   0.8190   3.2351  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.78381    0.34185   8.143 3.85e-16 ***
GSEGRD                        -0.50477    0.12347  -4.088 4.35e-05 ***
SalaryOverUnderIndAvg          0.21993    0.58786   0.374 0.708324    
LowerLimitAge                 -2.02125    0.11988 -16.860  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97233    0.10128   9.600  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14259    0.09474   1.505 0.132305    
BLS_FEDERAL_JobOpenings_Level  0.61455    0.06951   8.842  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03394    0.09841   0.345 0.730206    
LOSSqrt                       -6.61769    0.19390 -34.129  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20347    0.10576  -1.924 0.054362 .  
AGELVL_B                      -0.58653    0.22001  -2.666 0.007678 ** 
AGELVL_C                      -0.16779    0.08533  -1.966 0.049254 *  
AGELVL_E                       0.27407    0.06732   4.071 4.68e-05 ***
AGELVL_F                       0.48940    0.07015   6.976 3.03e-12 ***
AGELVL_G                       0.71224    0.07401   9.623  < 2e-16 ***
AGELVL_H                       0.84672    0.07623  11.107  < 2e-16 ***
AGELVL_I                       0.71764    0.08111   8.847  < 2e-16 ***
LOC_01                        -0.20859    0.17455  -1.195 0.232083    
LOC_02                         0.15555    0.25597   0.608 0.543406    
LOC_04                         0.52817    0.14713   3.590 0.000331 ***
LOC_05                         0.08277    0.23836   0.347 0.728420    
LOC_06                         0.41149    0.10065   4.088 4.35e-05 ***
LOC_08                         0.14493    0.13468   1.076 0.281892    
LOC_09                        -0.05656    0.33263  -0.170 0.864989    
LOC_10                         0.19773    0.58334   0.339 0.734644    
LOC_12                         0.03582    0.12915   0.277 0.781519    
LOC_13                         0.17059    0.12566   1.358 0.174603    
LOC_15                        -0.06775    0.16792  -0.403 0.686601    
LOC_16                         0.05356    0.27539   0.194 0.845806    
LOC_17                        -0.22071    0.15171  -1.455 0.145722    
LOC_18                        -0.22683    0.25532  -0.888 0.374317    
LOC_19                         0.21653    0.32317   0.670 0.502848    
LOC_20                         0.61459    0.26072   2.357 0.018410 *  
LOC_21                        -0.03030    0.20902  -0.145 0.884738    
LOC_22                        -0.11064    0.21320  -0.519 0.603787    
LOC_23                         0.07004    0.34026   0.206 0.836914    
LOC_24                        -0.10849    0.09421  -1.152 0.249486    
LOC_25                         0.14461    0.18686   0.774 0.438976    
LOC_26                        -0.07089    0.20654  -0.343 0.731407    
LOC_27                         0.28907    0.24242   1.192 0.233090    
LOC_28                        -0.12411    0.25064  -0.495 0.620487    
LOC_29                        -0.34928    0.17625  -1.982 0.047514 *  
LOC_30                         0.68132    0.24529   2.778 0.005476 ** 
LOC_31                         0.13363    0.33814   0.395 0.692692    
LOC_32                         0.23246    0.24849   0.935 0.349541    
LOC_33                         0.19846    0.52059   0.381 0.703042    
LOC_34                        -0.18157    0.20030  -0.906 0.364685    
LOC_35                         0.47339    0.15975   2.963 0.003043 ** 
LOC_36                        -0.02832    0.14832  -0.191 0.848601    
LOC_37                         0.06847    0.16003   0.428 0.668753    
LOC_38                         0.48947    0.36977   1.324 0.185597    
LOC_39                        -0.28995    0.15044  -1.927 0.053932 .  
LOC_40                         0.17874    0.17256   1.036 0.300293    
LOC_41                         0.11616    0.20266   0.573 0.566526    
LOC_42                        -0.29432    0.15076  -1.952 0.050904 .  
LOC_44                         0.33924    0.49359   0.687 0.491901    
LOC_45                         0.05953    0.22972   0.259 0.795515    
LOC_46                         0.81508    0.25905   3.146 0.001653 ** 
LOC_47                        -0.17826    0.21533  -0.828 0.407746    
LOC_48                         0.25220    0.10519   2.398 0.016500 *  
LOC_49                        -0.18419    0.21405  -0.861 0.389502    
LOC_50                        -0.40841    0.59473  -0.687 0.492256    
LOC_51                        -0.15484    0.09175  -1.688 0.091467 .  
LOC_53                         0.39852    0.13727   2.903 0.003694 ** 
LOC_54                        -0.06119    0.27043  -0.226 0.820980    
LOC_55                        -0.32049    0.25484  -1.258 0.208523    
TOA_15                         0.06828    0.06912   0.988 0.323244    
TOA_20                         1.12762    0.19280   5.849 4.96e-09 ***
TOA_30                         0.34614    0.08928   3.877 0.000106 ***
TOA_32                         0.42030    0.75397   0.557 0.577220    
TOA_35                        -0.53559    0.28671  -1.868 0.061749 .  
TOA_38                         0.32852    0.06731   4.881 1.06e-06 ***
TOA_40                        -0.10601    0.17648  -0.601 0.548033    
TOA_42                         1.14032    0.44139   2.583 0.009781 ** 
TOA_44                         2.16415    1.07265   2.018 0.043636 *  
PPGROUP_11                    -0.40970    0.11723  -3.495 0.000475 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14543  on 14844  degrees of freedom
AIC: 14695

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.709771
SalaryOverUnderIndAvg         1.362036
LowerLimitAge                 2.331493
BLS_FEDERAL_OtherSep_Rate     1.780582
BLS_FEDERAL_Quits_Rate        1.239390
BLS_FEDERAL_JobOpenings_Level 1.189508
BLS_FEDERAL_Layoffs_Rate      1.400708
LOSSqrt                       1.863360
SEPCount_EFDATE_OCCLog        1.110277
AGELVL_B                      1.168845
AGELVL_C                      1.661983
AGELVL_E                      1.407668
AGELVL_F                      1.284155
AGELVL_G                      1.271141
AGELVL_H                      1.360831
AGELVL_I                      1.516931
LOC_01                        1.155373
LOC_02                        1.077814
LOC_04                        1.297941
LOC_05                        1.086855
LOC_06                        1.643160
LOC_08                        1.281207
LOC_09                        1.043546
LOC_10                        1.015115
LOC_12                        1.333230
LOC_13                        1.347045
LOC_15                        1.173237
LOC_16                        1.070530
LOC_17                        1.194398
LOC_18                        1.073553
LOC_19                        1.046963
LOC_20                        1.074933
LOC_21                        1.115026
LOC_22                        1.105494
LOC_23                        1.041765
LOC_24                        1.659471
LOC_25                        1.116529
LOC_26                        1.113368
LOC_27                        1.083215
LOC_28                        1.076568
LOC_29                        1.166510
LOC_30                        1.092223
LOC_31                        1.044672
LOC_32                        1.080506
LOC_33                        1.019281
LOC_34                        1.108202
LOC_35                        1.235907
LOC_36                        1.212547
LOC_37                        1.199355
LOC_38                        1.038948
LOC_39                        1.218269
LOC_40                        1.167172
LOC_41                        1.121634
LOC_42                        1.204074
LOC_44                        1.018958
LOC_45                        1.093417
LOC_46                        1.088753
LOC_47                        1.104412
LOC_48                        1.597803
LOC_49                        1.103006
LOC_50                        1.014627
LOC_51                        1.785718
LOC_53                        1.278745
LOC_54                        1.060235
LOC_55                        1.077441
TOA_15                        1.405743
TOA_20                        1.028644
TOA_30                        1.249555
TOA_32                        1.005946
TOA_35                        1.078261
TOA_38                        1.177114
TOA_40                        1.063136
TOA_42                        1.010736
TOA_44                        1.004521
PPGROUP_11                    1.158849

Removed AFTER this step: LOC_21 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8258  -0.7389  -0.1369   0.8188   3.2357  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.77799    0.33949   8.183 2.77e-16 ***
GSEGRD                        -0.50275    0.12267  -4.098 4.16e-05 ***
SalaryOverUnderIndAvg          0.22377    0.58733   0.381  0.70321    
LowerLimitAge                 -2.02212    0.11973 -16.889  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97215    0.10127   9.599  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14277    0.09473   1.507  0.13178    
BLS_FEDERAL_JobOpenings_Level  0.61446    0.06950   8.841  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03398    0.09841   0.345  0.72985    
LOSSqrt                       -6.61790    0.19390 -34.131  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20415    0.10566  -1.932  0.05334 .  
AGELVL_B                      -0.58610    0.21999  -2.664  0.00772 ** 
AGELVL_C                      -0.16773    0.08533  -1.966  0.04933 *  
AGELVL_E                       0.27402    0.06732   4.070 4.70e-05 ***
AGELVL_F                       0.48941    0.07015   6.976 3.03e-12 ***
AGELVL_G                       0.71208    0.07400   9.622  < 2e-16 ***
AGELVL_H                       0.84664    0.07623  11.106  < 2e-16 ***
AGELVL_I                       0.71774    0.08111   8.849  < 2e-16 ***
LOC_01                        -0.20546    0.17320  -1.186  0.23554    
LOC_02                         0.15882    0.25497   0.623  0.53336    
LOC_04                         0.53164    0.14517   3.662  0.00025 ***
LOC_05                         0.08602    0.23730   0.363  0.71697    
LOC_06                         0.41452    0.09845   4.210 2.55e-05 ***
LOC_08                         0.14809    0.13290   1.114  0.26515    
LOC_09                        -0.05332    0.33188  -0.161  0.87236    
LOC_10                         0.20111    0.58284   0.345  0.73006    
LOC_12                         0.03902    0.12724   0.307  0.75911    
LOC_13                         0.17378    0.12371   1.405  0.16012    
LOC_15                        -0.06454    0.16645  -0.388  0.69819    
LOC_16                         0.05689    0.27443   0.207  0.83578    
LOC_17                        -0.21769    0.15027  -1.449  0.14744    
LOC_18                        -0.22363    0.25436  -0.879  0.37930    
LOC_19                         0.21979    0.32238   0.682  0.49538    
LOC_20                         0.61795    0.25968   2.380  0.01733 *  
LOC_22                        -0.10739    0.21201  -0.507  0.61248    
LOC_23                         0.07316    0.33957   0.215  0.82941    
LOC_24                        -0.10565    0.09214  -1.147  0.25156    
LOC_25                         0.14755    0.18575   0.794  0.42698    
LOC_26                        -0.06769    0.20534  -0.330  0.74167    
LOC_27                         0.29237    0.24135   1.211  0.22574    
LOC_28                        -0.12083    0.24961  -0.484  0.62833    
LOC_29                        -0.34599    0.17478  -1.980  0.04775 *  
LOC_30                         0.68477    0.24413   2.805  0.00503 ** 
LOC_31                         0.13693    0.33737   0.406  0.68483    
LOC_32                         0.23572    0.24747   0.953  0.34084    
LOC_33                         0.20176    0.52010   0.388  0.69807    
LOC_34                        -0.17862    0.19926  -0.896  0.37003    
LOC_35                         0.47682    0.15798   3.018  0.00254 ** 
LOC_36                        -0.02524    0.14679  -0.172  0.86350    
LOC_37                         0.07174    0.15843   0.453  0.65066    
LOC_38                         0.49285    0.36902   1.336  0.18169    
LOC_39                        -0.28680    0.14886  -1.927  0.05402 .  
LOC_40                         0.18199    0.17110   1.064  0.28748    
LOC_41                         0.11937    0.20144   0.593  0.55347    
LOC_42                        -0.29128    0.14929  -1.951  0.05104 .  
LOC_44                         0.34237    0.49315   0.694  0.48753    
LOC_45                         0.06287    0.22856   0.275  0.78327    
LOC_46                         0.81869    0.25785   3.175  0.00150 ** 
LOC_47                        -0.17499    0.21414  -0.817  0.41383    
LOC_48                         0.25545    0.10277   2.486  0.01293 *  
LOC_49                        -0.18096    0.21288  -0.850  0.39529    
LOC_50                        -0.40507    0.59427  -0.682  0.49548    
LOC_51                        -0.15183    0.08935  -1.699  0.08929 .  
LOC_53                         0.40168    0.13552   2.964  0.00304 ** 
LOC_54                        -0.05801    0.26953  -0.215  0.82958    
LOC_55                        -0.31727    0.25386  -1.250  0.21138    
TOA_15                         0.06818    0.06912   0.986  0.32391    
TOA_20                         1.12783    0.19281   5.850 4.93e-09 ***
TOA_30                         0.34707    0.08905   3.898 9.71e-05 ***
TOA_32                         0.41999    0.75401   0.557  0.57752    
TOA_35                        -0.53494    0.28668  -1.866  0.06204 .  
TOA_38                         0.32828    0.06729   4.879 1.07e-06 ***
TOA_40                        -0.10523    0.17640  -0.597  0.55079    
TOA_42                         1.14080    0.44138   2.585  0.00975 ** 
TOA_44                         2.16648    1.07253   2.020  0.04339 *  
PPGROUP_11                    -0.40912    0.11717  -3.492  0.00048 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14543  on 14845  degrees of freedom
AIC: 14693

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.687961
SalaryOverUnderIndAvg         1.359393
LowerLimitAge                 2.325664
BLS_FEDERAL_OtherSep_Rate     1.780297
BLS_FEDERAL_Quits_Rate        1.239183
BLS_FEDERAL_JobOpenings_Level 1.189397
BLS_FEDERAL_Layoffs_Rate      1.400682
LOSSqrt                       1.863295
SEPCount_EFDATE_OCCLog        1.108137
AGELVL_B                      1.168649
AGELVL_C                      1.661953
AGELVL_E                      1.407636
AGELVL_F                      1.284158
AGELVL_G                      1.270875
AGELVL_H                      1.360774
AGELVL_I                      1.516882
LOC_01                        1.137639
LOC_02                        1.069443
LOC_04                        1.263589
LOC_05                        1.077198
LOC_06                        1.572206
LOC_08                        1.247572
LOC_09                        1.038844
LOC_10                        1.013493
LOC_12                        1.294234
LOC_13                        1.305744
LOC_15                        1.152869
LOC_16                        1.063066
LOC_17                        1.171818
LOC_18                        1.065537
LOC_19                        1.041875
LOC_20                        1.066436
LOC_22                        1.093275
LOC_23                        1.037582
LOC_24                        1.587481
LOC_25                        1.103363
LOC_26                        1.100598
LOC_27                        1.073679
LOC_28                        1.067816
LOC_29                        1.147164
LOC_30                        1.081931
LOC_31                        1.039929
LOC_32                        1.071660
LOC_33                        1.017328
LOC_34                        1.096765
LOC_35                        1.208682
LOC_36                        1.187650
LOC_37                        1.175472
LOC_38                        1.034809
LOC_39                        1.192786
LOC_40                        1.147430
LOC_41                        1.108226
LOC_42                        1.180730
LOC_44                        1.017009
LOC_45                        1.082438
LOC_46                        1.078691
LOC_47                        1.092249
LOC_48                        1.525339
LOC_49                        1.091052
LOC_50                        1.013096
LOC_51                        1.693946
LOC_53                        1.246368
LOC_54                        1.053254
LOC_55                        1.069242
TOA_15                        1.405635
TOA_20                        1.028583
TOA_30                        1.243128
TOA_32                        1.005938
TOA_35                        1.077989
TOA_38                        1.176425
TOA_40                        1.062152
TOA_42                        1.010677
TOA_44                        1.004296
PPGROUP_11                    1.157491

Removed AFTER this step: LOC_09 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8257  -0.7391  -0.1369   0.8188   3.2362  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.77659    0.33939   8.181 2.81e-16 ***
GSEGRD                        -0.50164    0.12248  -4.096 4.21e-05 ***
SalaryOverUnderIndAvg          0.22140    0.58715   0.377 0.706118    
LowerLimitAge                 -2.02259    0.11970 -16.897  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97214    0.10127   9.599  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14283    0.09473   1.508 0.131633    
BLS_FEDERAL_JobOpenings_Level  0.61431    0.06950   8.839  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03401    0.09841   0.346 0.729666    
LOSSqrt                       -6.61754    0.19388 -34.132  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20414    0.10566  -1.932 0.053346 .  
AGELVL_B                      -0.58559    0.21997  -2.662 0.007764 ** 
AGELVL_C                      -0.16766    0.08533  -1.965 0.049429 *  
AGELVL_E                       0.27416    0.06732   4.073 4.65e-05 ***
AGELVL_F                       0.48948    0.07015   6.977 3.01e-12 ***
AGELVL_G                       0.71227    0.07399   9.626  < 2e-16 ***
AGELVL_H                       0.84675    0.07623  11.108  < 2e-16 ***
AGELVL_I                       0.71764    0.08111   8.848  < 2e-16 ***
LOC_01                        -0.20362    0.17282  -1.178 0.238719    
LOC_02                         0.16090    0.25463   0.632 0.527466    
LOC_04                         0.53364    0.14463   3.690 0.000225 ***
LOC_05                         0.08797    0.23699   0.371 0.710477    
LOC_06                         0.41649    0.09768   4.264 2.01e-05 ***
LOC_08                         0.14998    0.13237   1.133 0.257194    
LOC_10                         0.20324    0.58268   0.349 0.727241    
LOC_12                         0.04090    0.12670   0.323 0.746827    
LOC_13                         0.17563    0.12318   1.426 0.153930    
LOC_15                        -0.06258    0.16600  -0.377 0.706187    
LOC_16                         0.05888    0.27415   0.215 0.829957    
LOC_17                        -0.21583    0.14983  -1.441 0.149706    
LOC_18                        -0.22165    0.25406  -0.872 0.382975    
LOC_19                         0.22179    0.32214   0.688 0.491157    
LOC_20                         0.61992    0.25939   2.390 0.016850 *  
LOC_22                        -0.10543    0.21166  -0.498 0.618403    
LOC_23                         0.07513    0.33935   0.221 0.824795    
LOC_24                        -0.10390    0.09149  -1.136 0.256143    
LOC_25                         0.14941    0.18539   0.806 0.420288    
LOC_26                        -0.06571    0.20497  -0.321 0.748543    
LOC_27                         0.29440    0.24101   1.222 0.221896    
LOC_28                        -0.11887    0.24931  -0.477 0.633497    
LOC_29                        -0.34403    0.17435  -1.973 0.048474 *  
LOC_30                         0.68687    0.24378   2.818 0.004838 ** 
LOC_31                         0.13888    0.33716   0.412 0.680409    
LOC_32                         0.23775    0.24715   0.962 0.336065    
LOC_33                         0.20381    0.51994   0.392 0.695063    
LOC_34                        -0.17676    0.19892  -0.889 0.374233    
LOC_35                         0.47878    0.15750   3.040 0.002367 ** 
LOC_36                        -0.02333    0.14631  -0.159 0.873316    
LOC_37                         0.07368    0.15797   0.466 0.640904    
LOC_38                         0.49490    0.36880   1.342 0.179617    
LOC_39                        -0.28491    0.14839  -1.920 0.054855 .  
LOC_40                         0.18391    0.17068   1.077 0.281257    
LOC_41                         0.12134    0.20107   0.603 0.546185    
LOC_42                        -0.28947    0.14886  -1.945 0.051828 .  
LOC_44                         0.34437    0.49300   0.699 0.484861    
LOC_45                         0.06485    0.22822   0.284 0.776292    
LOC_46                         0.82080    0.25751   3.187 0.001436 ** 
LOC_47                        -0.17303    0.21379  -0.809 0.418320    
LOC_48                         0.25739    0.10205   2.522 0.011663 *  
LOC_49                        -0.17902    0.21253  -0.842 0.399616    
LOC_50                        -0.40309    0.59414  -0.678 0.497491    
LOC_51                        -0.14998    0.08861  -1.693 0.090533 .  
LOC_53                         0.40362    0.13498   2.990 0.002788 ** 
LOC_54                        -0.05610    0.26926  -0.208 0.834962    
LOC_55                        -0.31526    0.25355  -1.243 0.213726    
TOA_15                         0.06805    0.06911   0.985 0.324764    
TOA_20                         1.12812    0.19280   5.851 4.88e-09 ***
TOA_30                         0.34754    0.08900   3.905 9.43e-05 ***
TOA_32                         0.41972    0.75406   0.557 0.577788    
TOA_35                        -0.53432    0.28665  -1.864 0.062320 .  
TOA_38                         0.32802    0.06727   4.876 1.08e-06 ***
TOA_40                        -0.10508    0.17639  -0.596 0.551374    
TOA_42                         1.14114    0.44138   2.585 0.009726 ** 
TOA_44                         2.16788    1.07250   2.021 0.043244 *  
PPGROUP_11                    -0.40905    0.11717  -3.491 0.000481 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14543  on 14846  degrees of freedom
AIC: 14691

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.682643
SalaryOverUnderIndAvg         1.358526
LowerLimitAge                 2.324361
BLS_FEDERAL_OtherSep_Rate     1.780344
BLS_FEDERAL_Quits_Rate        1.239175
BLS_FEDERAL_JobOpenings_Level 1.189204
BLS_FEDERAL_Layoffs_Rate      1.400701
LOSSqrt                       1.863038
SEPCount_EFDATE_OCCLog        1.108135
AGELVL_B                      1.168388
AGELVL_C                      1.661927
AGELVL_E                      1.407413
AGELVL_F                      1.284126
AGELVL_G                      1.270561
AGELVL_H                      1.360678
AGELVL_I                      1.516775
LOC_01                        1.132667
LOC_02                        1.066681
LOC_04                        1.254313
LOC_05                        1.074380
LOC_06                        1.547704
LOC_08                        1.237732
LOC_10                        1.012969
LOC_12                        1.283232
LOC_13                        1.294435
LOC_15                        1.146631
LOC_16                        1.060904
LOC_17                        1.164865
LOC_18                        1.063023
LOC_19                        1.040327
LOC_20                        1.064050
LOC_22                        1.089639
LOC_23                        1.036233
LOC_24                        1.565215
LOC_25                        1.099081
LOC_26                        1.096610
LOC_27                        1.070737
LOC_28                        1.065270
LOC_29                        1.141599
LOC_30                        1.078836
LOC_31                        1.038588
LOC_32                        1.068864
LOC_33                        1.016711
LOC_34                        1.093039
LOC_35                        1.201465
LOC_36                        1.179866
LOC_37                        1.168641
LOC_38                        1.033569
LOC_39                        1.185309
LOC_40                        1.141838
LOC_41                        1.104098
LOC_42                        1.173979
LOC_44                        1.016359
LOC_45                        1.079273
LOC_46                        1.075906
LOC_47                        1.088696
LOC_48                        1.504064
LOC_49                        1.087529
LOC_50                        1.012661
LOC_51                        1.665897
LOC_53                        1.236436
LOC_54                        1.051193
LOC_55                        1.066639
TOA_15                        1.405445
TOA_20                        1.028496
TOA_30                        1.241826
TOA_32                        1.005933
TOA_35                        1.077785
TOA_38                        1.175781
TOA_40                        1.062126
TOA_42                        1.010652
TOA_44                        1.004229
PPGROUP_11                    1.157450

Removed AFTER this step: LOC_36 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8256  -0.7391  -0.1369   0.8186   3.2371  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.77179    0.33805   8.199 2.42e-16 ***
GSEGRD                        -0.49926    0.12156  -4.107 4.01e-05 ***
SalaryOverUnderIndAvg          0.22100    0.58715   0.376 0.706622    
LowerLimitAge                 -2.02302    0.11967 -16.905  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97201    0.10127   9.598  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14284    0.09473   1.508 0.131609    
BLS_FEDERAL_JobOpenings_Level  0.61442    0.06950   8.841  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03412    0.09841   0.347 0.728828    
LOSSqrt                       -6.61834    0.19382 -34.146  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20451    0.10563  -1.936 0.052864 .  
AGELVL_B                      -0.58530    0.21995  -2.661 0.007790 ** 
AGELVL_C                      -0.16765    0.08533  -1.965 0.049440 *  
AGELVL_E                       0.27401    0.06731   4.071 4.69e-05 ***
AGELVL_F                       0.48931    0.07015   6.976 3.05e-12 ***
AGELVL_G                       0.71213    0.07399   9.625  < 2e-16 ***
AGELVL_H                       0.84674    0.07623  11.108  < 2e-16 ***
AGELVL_I                       0.71778    0.08110   8.850  < 2e-16 ***
LOC_01                        -0.19969    0.17105  -1.167 0.243039    
LOC_02                         0.16510    0.25326   0.652 0.514460    
LOC_04                         0.53779    0.14227   3.780 0.000157 ***
LOC_05                         0.09208    0.23558   0.391 0.695898    
LOC_06                         0.42048    0.09443   4.453 8.47e-06 ***
LOC_08                         0.15395    0.13001   1.184 0.236349    
LOC_10                         0.20746    0.58205   0.356 0.721521    
LOC_12                         0.04488    0.12422   0.361 0.717851    
LOC_13                         0.17954    0.12071   1.487 0.136913    
LOC_15                        -0.05857    0.16408  -0.357 0.721125    
LOC_16                         0.06303    0.27291   0.231 0.817353    
LOC_17                        -0.21194    0.14782  -1.434 0.151637    
LOC_18                        -0.21758    0.25277  -0.861 0.389363    
LOC_19                         0.22590    0.32111   0.703 0.481749    
LOC_20                         0.62402    0.25810   2.418 0.015619 *  
LOC_22                        -0.10138    0.21013  -0.482 0.629481    
LOC_23                         0.07918    0.33840   0.234 0.814997    
LOC_24                        -0.10027    0.08862  -1.131 0.257852    
LOC_25                         0.15328    0.18379   0.834 0.404307    
LOC_26                        -0.06157    0.20331  -0.303 0.762028    
LOC_27                         0.29861    0.23955   1.247 0.212559    
LOC_28                        -0.11482    0.24800  -0.463 0.643378    
LOC_29                        -0.33995    0.17246  -1.971 0.048702 *  
LOC_30                         0.69113    0.24230   2.852 0.004340 ** 
LOC_31                         0.14302    0.33615   0.425 0.670499    
LOC_32                         0.24186    0.24580   0.984 0.325121    
LOC_33                         0.20809    0.51926   0.401 0.688614    
LOC_34                        -0.17287    0.19742  -0.876 0.381219    
LOC_35                         0.48293    0.15534   3.109 0.001878 ** 
LOC_37                         0.07775    0.15589   0.499 0.617944    
LOC_38                         0.49913    0.36783   1.357 0.174791    
LOC_39                        -0.28091    0.14625  -1.921 0.054767 .  
LOC_40                         0.18794    0.16880   1.113 0.265539    
LOC_41                         0.12542    0.19943   0.629 0.529422    
LOC_42                        -0.28557    0.14684  -1.945 0.051802 .  
LOC_44                         0.34839    0.49238   0.708 0.479219    
LOC_45                         0.06898    0.22675   0.304 0.760963    
LOC_46                         0.82517    0.25604   3.223 0.001269 ** 
LOC_47                        -0.16896    0.21226  -0.796 0.426036    
LOC_48                         0.26142    0.09888   2.644 0.008201 ** 
LOC_49                        -0.17501    0.21104  -0.829 0.406951    
LOC_50                        -0.39906    0.59359  -0.672 0.501407    
LOC_51                        -0.14616    0.08531  -1.713 0.086653 .  
LOC_53                         0.40765    0.13259   3.075 0.002108 ** 
LOC_54                        -0.05214    0.26810  -0.194 0.845804    
LOC_55                        -0.31112    0.25221  -1.234 0.217366    
TOA_15                         0.06799    0.06911   0.984 0.325235    
TOA_20                         1.12832    0.19280   5.852 4.85e-09 ***
TOA_30                         0.34805    0.08894   3.913 9.11e-05 ***
TOA_32                         0.41940    0.75411   0.556 0.578107    
TOA_35                        -0.53379    0.28664  -1.862 0.062570 .  
TOA_38                         0.32756    0.06721   4.874 1.09e-06 ***
TOA_40                        -0.10442    0.17634  -0.592 0.553758    
TOA_42                         1.14133    0.44138   2.586 0.009714 ** 
TOA_44                         2.17089    1.07233   2.024 0.042924 *  
PPGROUP_11                    -0.40866    0.11715  -3.488 0.000486 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14847  degrees of freedom
AIC: 14690

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.657660
SalaryOverUnderIndAvg         1.358527
LowerLimitAge                 2.323183
BLS_FEDERAL_OtherSep_Rate     1.780218
BLS_FEDERAL_Quits_Rate        1.239155
BLS_FEDERAL_JobOpenings_Level 1.189094
BLS_FEDERAL_Layoffs_Rate      1.400661
LOSSqrt                       1.861913
SEPCount_EFDATE_OCCLog        1.107623
AGELVL_B                      1.168345
AGELVL_C                      1.661945
AGELVL_E                      1.407111
AGELVL_F                      1.283799
AGELVL_G                      1.270373
AGELVL_H                      1.360653
AGELVL_I                      1.516606
LOC_01                        1.109664
LOC_02                        1.055243
LOC_04                        1.213554
LOC_05                        1.061690
LOC_06                        1.446196
LOC_08                        1.193965
LOC_10                        1.010876
LOC_12                        1.233387
LOC_13                        1.243048
LOC_15                        1.120307
LOC_16                        1.051323
LOC_17                        1.133941
LOC_18                        1.052290
LOC_19                        1.033660
LOC_20                        1.053602
LOC_22                        1.073896
LOC_23                        1.030415
LOC_24                        1.468350
LOC_25                        1.080265
LOC_26                        1.079018
LOC_27                        1.057836
LOC_28                        1.054198
LOC_29                        1.116958
LOC_30                        1.065860
LOC_31                        1.032410
LOC_32                        1.057221
LOC_33                        1.014011
LOC_34                        1.076617
LOC_35                        1.168674
LOC_37                        1.138096
LOC_38                        1.028209
LOC_39                        1.151499
LOC_40                        1.116769
LOC_41                        1.086205
LOC_42                        1.142333
LOC_44                        1.013695
LOC_45                        1.065360
LOC_46                        1.063668
LOC_47                        1.073126
LOC_48                        1.412089
LOC_49                        1.072259
LOC_50                        1.010826
LOC_51                        1.544234
LOC_53                        1.193008
LOC_54                        1.042254
LOC_55                        1.055436
TOA_15                        1.405458
TOA_20                        1.028455
TOA_30                        1.240230
TOA_32                        1.005925
TOA_35                        1.077661
TOA_38                        1.173642
TOA_40                        1.061551
TOA_42                        1.010642
TOA_44                        1.003920
PPGROUP_11                    1.156931

Removed AFTER this step: LOC_54 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8257  -0.7396  -0.1369   0.8188   3.2376  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.76729    0.33725   8.205 2.30e-16 ***
GSEGRD                        -0.49829    0.12146  -4.103 4.09e-05 ***
SalaryOverUnderIndAvg          0.22579    0.58664   0.385 0.700315    
LowerLimitAge                 -2.02341    0.11965 -16.911  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97222    0.10126   9.601  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14258    0.09472   1.505 0.132269    
BLS_FEDERAL_JobOpenings_Level  0.61440    0.06950   8.841  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03414    0.09841   0.347 0.728648    
LOSSqrt                       -6.61889    0.19381 -34.152  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20472    0.10563  -1.938 0.052605 .  
AGELVL_B                      -0.58485    0.21994  -2.659 0.007834 ** 
AGELVL_C                      -0.16744    0.08532  -1.962 0.049707 *  
AGELVL_E                       0.27409    0.06731   4.072 4.66e-05 ***
AGELVL_F                       0.48934    0.07015   6.976 3.03e-12 ***
AGELVL_G                       0.71218    0.07398   9.626  < 2e-16 ***
AGELVL_H                       0.84681    0.07623  11.109  < 2e-16 ***
AGELVL_I                       0.71774    0.08110   8.850  < 2e-16 ***
LOC_01                        -0.19735    0.17063  -1.157 0.247438    
LOC_02                         0.16738    0.25299   0.662 0.508226    
LOC_04                         0.54026    0.14170   3.813 0.000137 ***
LOC_05                         0.09451    0.23524   0.402 0.687858    
LOC_06                         0.42264    0.09377   4.507 6.56e-06 ***
LOC_08                         0.15623    0.12948   1.207 0.227592    
LOC_10                         0.20996    0.58189   0.361 0.718237    
LOC_12                         0.04725    0.12362   0.382 0.702268    
LOC_13                         0.18186    0.12011   1.514 0.130008    
LOC_15                        -0.05631    0.16367  -0.344 0.730806    
LOC_16                         0.06548    0.27262   0.240 0.810172    
LOC_17                        -0.20974    0.14739  -1.423 0.154724    
LOC_18                        -0.21516    0.25247  -0.852 0.394088    
LOC_19                         0.22837    0.32086   0.712 0.476621    
LOC_20                         0.62648    0.25779   2.430 0.015092 *  
LOC_22                        -0.09900    0.20977  -0.472 0.636978    
LOC_23                         0.08146    0.33820   0.241 0.809668    
LOC_24                        -0.09819    0.08797  -1.116 0.264344    
LOC_25                         0.15539    0.18347   0.847 0.397018    
LOC_26                        -0.05924    0.20296  -0.292 0.770395    
LOC_27                         0.30099    0.23923   1.258 0.208339    
LOC_28                        -0.11239    0.24769  -0.454 0.650014    
LOC_29                        -0.33754    0.17201  -1.962 0.049728 *  
LOC_30                         0.69355    0.24199   2.866 0.004156 ** 
LOC_31                         0.14547    0.33592   0.433 0.664979    
LOC_32                         0.24429    0.24548   0.995 0.319647    
LOC_33                         0.21058    0.51910   0.406 0.684997    
LOC_34                        -0.17075    0.19712  -0.866 0.386370    
LOC_35                         0.48536    0.15484   3.135 0.001721 ** 
LOC_37                         0.08013    0.15541   0.516 0.606119    
LOC_38                         0.50157    0.36761   1.364 0.172440    
LOC_39                        -0.27859    0.14576  -1.911 0.055976 .  
LOC_40                         0.19027    0.16838   1.130 0.258468    
LOC_41                         0.12781    0.19906   0.642 0.520836    
LOC_42                        -0.28333    0.14639  -1.935 0.052931 .  
LOC_44                         0.35068    0.49226   0.712 0.476221    
LOC_45                         0.07136    0.22642   0.315 0.752626    
LOC_46                         0.82769    0.25572   3.237 0.001209 ** 
LOC_47                        -0.16654    0.21189  -0.786 0.431883    
LOC_48                         0.26374    0.09816   2.687 0.007212 ** 
LOC_49                        -0.17264    0.21068  -0.819 0.412554    
LOC_50                        -0.39653    0.59345  -0.668 0.504015    
LOC_51                        -0.14399    0.08457  -1.703 0.088650 .  
LOC_53                         0.40992    0.13208   3.104 0.001911 ** 
LOC_55                        -0.30868    0.25190  -1.225 0.220422    
TOA_15                         0.06799    0.06911   0.984 0.325227    
TOA_20                         1.12869    0.19279   5.855 4.78e-09 ***
TOA_30                         0.34895    0.08883   3.928 8.55e-05 ***
TOA_32                         0.41932    0.75413   0.556 0.578187    
TOA_35                        -0.53324    0.28663  -1.860 0.062829 .  
TOA_38                         0.32721    0.06718   4.870 1.11e-06 ***
TOA_40                        -0.10381    0.17632  -0.589 0.556003    
TOA_42                         1.14046    0.44138   2.584 0.009771 ** 
TOA_44                         2.17281    1.07229   2.026 0.042731 *  
PPGROUP_11                    -0.40883    0.11715  -3.490 0.000483 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14848  degrees of freedom
AIC: 14688

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.654900
SalaryOverUnderIndAvg         1.356144
LowerLimitAge                 2.322537
BLS_FEDERAL_OtherSep_Rate     1.779967
BLS_FEDERAL_Quits_Rate        1.238896
BLS_FEDERAL_JobOpenings_Level 1.189080
BLS_FEDERAL_Layoffs_Rate      1.400649
LOSSqrt                       1.861550
SEPCount_EFDATE_OCCLog        1.107501
AGELVL_B                      1.168203
AGELVL_C                      1.661700
AGELVL_E                      1.407073
AGELVL_F                      1.283799
AGELVL_G                      1.270386
AGELVL_H                      1.360623
AGELVL_I                      1.516608
LOC_01                        1.104134
LOC_02                        1.052987
LOC_04                        1.203869
LOC_05                        1.058690
LOC_06                        1.426053
LOC_08                        1.184262
LOC_10                        1.010384
LOC_12                        1.221491
LOC_13                        1.230847
LOC_15                        1.114678
LOC_16                        1.049069
LOC_17                        1.127233
LOC_18                        1.049736
LOC_19                        1.032040
LOC_20                        1.051063
LOC_22                        1.070241
LOC_23                        1.029175
LOC_24                        1.446867
LOC_25                        1.076466
LOC_26                        1.075257
LOC_27                        1.055076
LOC_28                        1.051507
LOC_29                        1.111154
LOC_30                        1.063039
LOC_31                        1.030958
LOC_32                        1.054476
LOC_33                        1.013394
LOC_34                        1.073303
LOC_35                        1.161118
LOC_37                        1.131070
LOC_38                        1.027011
LOC_39                        1.143769
LOC_40                        1.111134
LOC_41                        1.082089
LOC_42                        1.135238
LOC_44                        1.013110
LOC_45                        1.062247
LOC_46                        1.060953
LOC_47                        1.069456
LOC_48                        1.391448
LOC_49                        1.068675
LOC_50                        1.010342
LOC_51                        1.517656
LOC_53                        1.183730
LOC_55                        1.052816
TOA_15                        1.405450
TOA_20                        1.028352
TOA_30                        1.236945
TOA_32                        1.005925
TOA_35                        1.077547
TOA_38                        1.172736
TOA_40                        1.061219
TOA_42                        1.010531
TOA_44                        1.003834
PPGROUP_11                    1.156872

Removed AFTER this step: LOC_16 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8259  -0.7395  -0.1369   0.8187   3.2369  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.77580    0.33539   8.276  < 2e-16 ***
GSEGRD                        -0.50116    0.12087  -4.146 3.38e-05 ***
SalaryOverUnderIndAvg          0.21776    0.58566   0.372 0.710021    
LowerLimitAge                 -2.02295    0.11963 -16.910  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97184    0.10125   9.599  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14247    0.09472   1.504 0.132570    
BLS_FEDERAL_JobOpenings_Level  0.61459    0.06949   8.844  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03446    0.09840   0.350 0.726209    
LOSSqrt                       -6.61662    0.19356 -34.183  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20523    0.10561  -1.943 0.051975 .  
AGELVL_B                      -0.58524    0.21993  -2.661 0.007790 ** 
AGELVL_C                      -0.16742    0.08532  -1.962 0.049730 *  
AGELVL_E                       0.27405    0.06731   4.072 4.67e-05 ***
AGELVL_F                       0.48965    0.07013   6.982 2.92e-12 ***
AGELVL_G                       0.71226    0.07399   9.627  < 2e-16 ***
AGELVL_H                       0.84696    0.07623  11.111  < 2e-16 ***
AGELVL_I                       0.71795    0.08110   8.853  < 2e-16 ***
LOC_01                        -0.20026    0.17019  -1.177 0.239318    
LOC_02                         0.16441    0.25268   0.651 0.515262    
LOC_04                         0.53710    0.14108   3.807 0.000141 ***
LOC_05                         0.09143    0.23489   0.389 0.697093    
LOC_06                         0.42001    0.09312   4.510 6.48e-06 ***
LOC_08                         0.15341    0.12894   1.190 0.234154    
LOC_10                         0.20693    0.58175   0.356 0.722062    
LOC_12                         0.04430    0.12300   0.360 0.718710    
LOC_13                         0.17895    0.11950   1.498 0.134259    
LOC_15                        -0.05906    0.16327  -0.362 0.717530    
LOC_17                        -0.21244    0.14695  -1.446 0.148290    
LOC_18                        -0.21804    0.25217  -0.865 0.387234    
LOC_19                         0.22522    0.32057   0.703 0.482337    
LOC_20                         0.62335    0.25745   2.421 0.015467 *  
LOC_22                        -0.10201    0.20939  -0.487 0.626127    
LOC_23                         0.07852    0.33796   0.232 0.816269    
LOC_24                        -0.10064    0.08737  -1.152 0.249362    
LOC_25                         0.15278    0.18314   0.834 0.404166    
LOC_26                        -0.06211    0.20260  -0.307 0.759177    
LOC_27                         0.29797    0.23890   1.247 0.212309    
LOC_28                        -0.11547    0.24734  -0.467 0.640603    
LOC_29                        -0.34060    0.17153  -1.986 0.047075 *  
LOC_30                         0.69031    0.24160   2.857 0.004273 ** 
LOC_31                         0.14227    0.33564   0.424 0.671648    
LOC_32                         0.24131    0.24515   0.984 0.324962    
LOC_33                         0.20757    0.51892   0.400 0.689157    
LOC_34                        -0.17337    0.19681  -0.881 0.378371    
LOC_35                         0.48223    0.15428   3.126 0.001775 ** 
LOC_37                         0.07726    0.15494   0.499 0.618043    
LOC_38                         0.49833    0.36735   1.357 0.174921    
LOC_39                        -0.28145    0.14527  -1.937 0.052698 .  
LOC_40                         0.18729    0.16791   1.115 0.264686    
LOC_41                         0.12469    0.19863   0.628 0.530157    
LOC_42                        -0.28603    0.14595  -1.960 0.050015 .  
LOC_44                         0.34779    0.49207   0.707 0.479689    
LOC_45                         0.06833    0.22606   0.302 0.762458    
LOC_46                         0.82442    0.25534   3.229 0.001244 ** 
LOC_47                        -0.16952    0.21152  -0.801 0.422874    
LOC_48                         0.26087    0.09743   2.678 0.007416 ** 
LOC_49                        -0.17564    0.21031  -0.835 0.403642    
LOC_50                        -0.39943    0.59331  -0.673 0.500805    
LOC_51                        -0.14665    0.08385  -1.749 0.080279 .  
LOC_53                         0.40703    0.13152   3.095 0.001970 ** 
LOC_55                        -0.31165    0.25158  -1.239 0.215432    
TOA_15                         0.06772    0.06910   0.980 0.327049    
TOA_20                         1.12828    0.19278   5.853 4.83e-09 ***
TOA_30                         0.34825    0.08878   3.923 8.75e-05 ***
TOA_32                         0.41972    0.75408   0.557 0.577802    
TOA_35                        -0.53453    0.28658  -1.865 0.062149 .  
TOA_38                         0.32717    0.06718   4.870 1.12e-06 ***
TOA_40                        -0.10427    0.17631  -0.591 0.554262    
TOA_42                         1.13985    0.44138   2.582 0.009810 ** 
TOA_44                         2.17087    1.07225   2.025 0.042908 *  
PPGROUP_11                    -0.40948    0.11711  -3.496 0.000471 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14849  degrees of freedom
AIC: 14686

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.638746
SalaryOverUnderIndAvg         1.351672
LowerLimitAge                 2.321901
BLS_FEDERAL_OtherSep_Rate     1.779528
BLS_FEDERAL_Quits_Rate        1.238885
BLS_FEDERAL_JobOpenings_Level 1.188952
BLS_FEDERAL_Layoffs_Rate      1.400393
LOSSqrt                       1.856960
SEPCount_EFDATE_OCCLog        1.107038
AGELVL_B                      1.168157
AGELVL_C                      1.661675
AGELVL_E                      1.407045
AGELVL_F                      1.283355
AGELVL_G                      1.270317
AGELVL_H                      1.360518
AGELVL_I                      1.516401
LOC_01                        1.098556
LOC_02                        1.050485
LOC_04                        1.193535
LOC_05                        1.055547
LOC_06                        1.406677
LOC_08                        1.174566
LOC_10                        1.009912
LOC_12                        1.209466
LOC_13                        1.218387
LOC_15                        1.109244
LOC_17                        1.120707
LOC_18                        1.047377
LOC_19                        1.030321
LOC_20                        1.048376
LOC_22                        1.066426
LOC_23                        1.027841
LOC_24                        1.427402
LOC_25                        1.072694
LOC_26                        1.071532
LOC_27                        1.052160
LOC_28                        1.048686
LOC_29                        1.105063
LOC_30                        1.059740
LOC_31                        1.029346
LOC_32                        1.051781
LOC_33                        1.012808
LOC_34                        1.070013
LOC_35                        1.152912
LOC_37                        1.124396
LOC_38                        1.025631
LOC_39                        1.136152
LOC_40                        1.105113
LOC_41                        1.077513
LOC_42                        1.128536
LOC_44                        1.012508
LOC_45                        1.058950
LOC_46                        1.057952
LOC_47                        1.065801
LOC_48                        1.370902
LOC_49                        1.064935
LOC_50                        1.009927
LOC_51                        1.491672
LOC_53                        1.173919
LOC_55                        1.050285
TOA_15                        1.405102
TOA_20                        1.028264
TOA_30                        1.235620
TOA_32                        1.005920
TOA_35                        1.077184
TOA_38                        1.172716
TOA_40                        1.061095
TOA_42                        1.010499
TOA_44                        1.003777
PPGROUP_11                    1.156240

Removed AFTER this step: LOC_23 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8260  -0.7397  -0.1369   0.8187   3.2365  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.77897    0.33511   8.293  < 2e-16 ***
GSEGRD                        -0.50242    0.12075  -4.161 3.17e-05 ***
SalaryOverUnderIndAvg          0.21757    0.58564   0.372 0.710264    
LowerLimitAge                 -2.02292    0.11963 -16.909  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97165    0.10124   9.597  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14262    0.09472   1.506 0.132148    
BLS_FEDERAL_JobOpenings_Level  0.61435    0.06949   8.842  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03429    0.09840   0.348 0.727502    
LOSSqrt                       -6.61600    0.19354 -34.184  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20555    0.10560  -1.947 0.051586 .  
AGELVL_B                      -0.58302    0.21972  -2.654 0.007966 ** 
AGELVL_C                      -0.16731    0.08532  -1.961 0.049880 *  
AGELVL_E                       0.27390    0.06731   4.069 4.71e-05 ***
AGELVL_F                       0.48956    0.07013   6.980 2.94e-12 ***
AGELVL_G                       0.71231    0.07398   9.628  < 2e-16 ***
AGELVL_H                       0.84700    0.07622  11.112  < 2e-16 ***
AGELVL_I                       0.71816    0.08109   8.856  < 2e-16 ***
LOC_01                        -0.20230    0.16997  -1.190 0.233961    
LOC_02                         0.16225    0.25251   0.643 0.520500    
LOC_04                         0.53500    0.14079   3.800 0.000145 ***
LOC_05                         0.08937    0.23472   0.381 0.703395    
LOC_06                         0.41802    0.09273   4.508 6.55e-06 ***
LOC_08                         0.15141    0.12866   1.177 0.239263    
LOC_10                         0.20485    0.58167   0.352 0.724711    
LOC_12                         0.04229    0.12270   0.345 0.730350    
LOC_13                         0.17697    0.11919   1.485 0.137625    
LOC_15                        -0.06109    0.16303  -0.375 0.707854    
LOC_17                        -0.21442    0.14670  -1.462 0.143862    
LOC_18                        -0.22002    0.25203  -0.873 0.382669    
LOC_19                         0.22309    0.32044   0.696 0.486294    
LOC_20                         0.62123    0.25728   2.415 0.015753 *  
LOC_22                        -0.10406    0.20920  -0.497 0.618910    
LOC_24                        -0.10250    0.08701  -1.178 0.238768    
LOC_25                         0.15083    0.18295   0.824 0.409700    
LOC_26                        -0.06416    0.20241  -0.317 0.751252    
LOC_27                         0.29583    0.23873   1.239 0.215273    
LOC_28                        -0.11755    0.24718  -0.476 0.634400    
LOC_29                        -0.34267    0.17130  -2.000 0.045464 *  
LOC_30                         0.68812    0.24141   2.850 0.004366 ** 
LOC_31                         0.14012    0.33550   0.418 0.676204    
LOC_32                         0.23926    0.24499   0.977 0.328762    
LOC_33                         0.20535    0.51884   0.396 0.692259    
LOC_34                        -0.17542    0.19662  -0.892 0.372279    
LOC_35                         0.48014    0.15402   3.117 0.001825 ** 
LOC_37                         0.07524    0.15470   0.486 0.626693    
LOC_38                         0.49612    0.36721   1.351 0.176688    
LOC_39                        -0.28348    0.14501  -1.955 0.050596 .  
LOC_40                         0.18525    0.16768   1.105 0.269263    
LOC_41                         0.12256    0.19841   0.618 0.536760    
LOC_42                        -0.28804    0.14569  -1.977 0.048035 *  
LOC_44                         0.34568    0.49197   0.703 0.482286    
LOC_45                         0.06629    0.22589   0.293 0.769168    
LOC_46                         0.82225    0.25517   3.222 0.001271 ** 
LOC_47                        -0.17158    0.21134  -0.812 0.416850    
LOC_48                         0.25887    0.09705   2.667 0.007643 ** 
LOC_49                        -0.17770    0.21012  -0.846 0.397728    
LOC_50                        -0.40146    0.59323  -0.677 0.498578    
LOC_51                        -0.14864    0.08341  -1.782 0.074736 .  
LOC_53                         0.40492    0.13121   3.086 0.002028 ** 
LOC_55                        -0.31374    0.25142  -1.248 0.212070    
TOA_15                         0.06773    0.06910   0.980 0.327002    
TOA_20                         1.12789    0.19277   5.851 4.89e-09 ***
TOA_30                         0.34757    0.08873   3.917 8.95e-05 ***
TOA_32                         0.41981    0.75405   0.557 0.577705    
TOA_35                        -0.53590    0.28651  -1.870 0.061420 .  
TOA_38                         0.32704    0.06718   4.868 1.13e-06 ***
TOA_40                        -0.10484    0.17629  -0.595 0.552046    
TOA_42                         1.13935    0.44138   2.581 0.009841 ** 
TOA_44                         2.16939    1.07223   2.023 0.043047 *  
PPGROUP_11                    -0.40964    0.11711  -3.498 0.000469 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14850  degrees of freedom
AIC: 14684

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.635493
SalaryOverUnderIndAvg         1.351637
LowerLimitAge                 2.321890
BLS_FEDERAL_OtherSep_Rate     1.779447
BLS_FEDERAL_Quits_Rate        1.238832
BLS_FEDERAL_JobOpenings_Level 1.188706
BLS_FEDERAL_Layoffs_Rate      1.400361
LOSSqrt                       1.856547
SEPCount_EFDATE_OCCLog        1.106852
AGELVL_B                      1.166101
AGELVL_C                      1.661686
AGELVL_E                      1.406940
AGELVL_F                      1.283334
AGELVL_G                      1.270330
AGELVL_H                      1.360533
AGELVL_I                      1.516174
LOC_01                        1.095654
LOC_02                        1.049075
LOC_04                        1.188659
LOC_05                        1.054045
LOC_06                        1.394802
LOC_08                        1.169349
LOC_10                        1.009673
LOC_12                        1.203474
LOC_13                        1.212179
LOC_15                        1.106082
LOC_17                        1.116947
LOC_18                        1.046191
LOC_19                        1.029487
LOC_20                        1.047073
LOC_22                        1.064552
LOC_24                        1.415517
LOC_25                        1.070446
LOC_26                        1.069504
LOC_27                        1.050601
LOC_28                        1.047330
LOC_29                        1.102109
LOC_30                        1.058144
LOC_31                        1.028569
LOC_32                        1.050431
LOC_33                        1.012465
LOC_34                        1.067863
LOC_35                        1.149027
LOC_37                        1.120893
LOC_38                        1.024947
LOC_39                        1.132050
LOC_40                        1.102110
LOC_41                        1.075232
LOC_42                        1.124592
LOC_44                        1.012161
LOC_45                        1.057364
LOC_46                        1.056550
LOC_47                        1.063935
LOC_48                        1.360220
LOC_49                        1.063052
LOC_50                        1.009711
LOC_51                        1.476127
LOC_53                        1.168301
LOC_55                        1.048946
TOA_15                        1.405138
TOA_20                        1.028186
TOA_30                        1.234263
TOA_32                        1.005920
TOA_35                        1.076690
TOA_38                        1.172635
TOA_40                        1.060882
TOA_42                        1.010477
TOA_44                        1.003742
PPGROUP_11                    1.156210

Removed AFTER this step: LOC_45 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8264  -0.7398  -0.1369   0.8186   3.2359  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.78877    0.33345   8.363  < 2e-16 ***
GSEGRD                        -0.50554    0.12028  -4.203 2.63e-05 ***
SalaryOverUnderIndAvg          0.20995    0.58507   0.359 0.719708    
LowerLimitAge                 -2.02189    0.11957 -16.909  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97177    0.10124   9.598  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14236    0.09472   1.503 0.132832    
BLS_FEDERAL_JobOpenings_Level  0.61423    0.06948   8.840  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03437    0.09839   0.349 0.726862    
LOSSqrt                       -6.61620    0.19354 -34.185  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20574    0.10559  -1.948 0.051365 .  
AGELVL_B                      -0.58404    0.21969  -2.658 0.007851 ** 
AGELVL_C                      -0.16743    0.08532  -1.962 0.049720 *  
AGELVL_E                       0.27385    0.06731   4.069 4.73e-05 ***
AGELVL_F                       0.48990    0.07012   6.986 2.82e-12 ***
AGELVL_G                       0.71286    0.07396   9.639  < 2e-16 ***
AGELVL_H                       0.84744    0.07621  11.120  < 2e-16 ***
AGELVL_I                       0.71843    0.08109   8.860  < 2e-16 ***
LOC_01                        -0.20609    0.16948  -1.216 0.223980    
LOC_02                         0.15833    0.25216   0.628 0.530074    
LOC_04                         0.53081    0.14007   3.790 0.000151 ***
LOC_05                         0.08535    0.23433   0.364 0.715696    
LOC_06                         0.41444    0.09193   4.508 6.53e-06 ***
LOC_08                         0.14764    0.12802   1.153 0.248811    
LOC_10                         0.20076    0.58155   0.345 0.729929    
LOC_12                         0.03837    0.12197   0.315 0.753075    
LOC_13                         0.17314    0.11848   1.461 0.143930    
LOC_15                        -0.06485    0.16253  -0.399 0.689882    
LOC_17                        -0.21804    0.14619  -1.492 0.135829    
LOC_18                        -0.22386    0.25169  -0.889 0.373782    
LOC_19                         0.21905    0.32015   0.684 0.493838    
LOC_20                         0.61715    0.25692   2.402 0.016298 *  
LOC_22                        -0.10803    0.20877  -0.517 0.604829    
LOC_24                        -0.10584    0.08626  -1.227 0.219857    
LOC_25                         0.14732    0.18256   0.807 0.419696    
LOC_26                        -0.06805    0.20199  -0.337 0.736181    
LOC_27                         0.29184    0.23835   1.224 0.220796    
LOC_28                        -0.12156    0.24681  -0.493 0.622356    
LOC_29                        -0.34667    0.17077  -2.030 0.042352 *  
LOC_30                         0.68391    0.24098   2.838 0.004540 ** 
LOC_31                         0.13597    0.33520   0.406 0.685020    
LOC_32                         0.23520    0.24460   0.962 0.336282    
LOC_33                         0.20126    0.51865   0.388 0.697978    
LOC_34                        -0.17892    0.19626  -0.912 0.361961    
LOC_35                         0.47603    0.15339   3.103 0.001913 ** 
LOC_37                         0.07133    0.15413   0.463 0.643497    
LOC_38                         0.49188    0.36694   1.340 0.180084    
LOC_39                        -0.28728    0.14444  -1.989 0.046703 *  
LOC_40                         0.18134    0.16715   1.085 0.277980    
LOC_41                         0.11854    0.19794   0.599 0.549253    
LOC_42                        -0.29167    0.14517  -2.009 0.044519 *  
LOC_44                         0.34190    0.49179   0.695 0.486913    
LOC_46                         0.81793    0.25475   3.211 0.001324 ** 
LOC_47                        -0.17558    0.21090  -0.833 0.405121    
LOC_48                         0.25500    0.09615   2.652 0.008000 ** 
LOC_49                        -0.18162    0.20970  -0.866 0.386433    
LOC_50                        -0.40559    0.59308  -0.684 0.494059    
LOC_51                        -0.15220    0.08253  -1.844 0.065154 .  
LOC_53                         0.40114    0.13058   3.072 0.002126 ** 
LOC_55                        -0.31771    0.25106  -1.265 0.205694    
TOA_15                         0.06730    0.06909   0.974 0.329997    
TOA_20                         1.12785    0.19281   5.849 4.93e-09 ***
TOA_30                         0.34676    0.08868   3.910 9.22e-05 ***
TOA_32                         0.42018    0.75402   0.557 0.577355    
TOA_35                        -0.53687    0.28648  -1.874 0.060925 .  
TOA_38                         0.32735    0.06717   4.874 1.10e-06 ***
TOA_40                        -0.10548    0.17628  -0.598 0.549608    
TOA_42                         1.13938    0.44136   2.581 0.009837 ** 
TOA_44                         2.16672    1.07219   2.021 0.043296 *  
PPGROUP_11                    -0.41045    0.11707  -3.506 0.000455 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14851  degrees of freedom
AIC: 14682

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.622738
SalaryOverUnderIndAvg         1.348899
LowerLimitAge                 2.319650
BLS_FEDERAL_OtherSep_Rate     1.779386
BLS_FEDERAL_Quits_Rate        1.238715
BLS_FEDERAL_JobOpenings_Level 1.188649
BLS_FEDERAL_Layoffs_Rate      1.400307
LOSSqrt                       1.856367
SEPCount_EFDATE_OCCLog        1.106791
AGELVL_B                      1.165822
AGELVL_C                      1.661625
AGELVL_E                      1.406918
AGELVL_F                      1.282980
AGELVL_G                      1.269558
AGELVL_H                      1.360004
AGELVL_I                      1.515946
LOC_01                        1.089328
LOC_02                        1.046139
LOC_04                        1.176431
LOC_05                        1.050457
LOC_06                        1.370739
LOC_08                        1.157708
LOC_10                        1.009095
LOC_12                        1.189254
LOC_13                        1.197700
LOC_15                        1.099290
LOC_17                        1.109069
LOC_18                        1.043379
LOC_19                        1.027585
LOC_20                        1.044017
LOC_22                        1.060106
LOC_24                        1.391427
LOC_25                        1.065889
LOC_26                        1.064928
LOC_27                        1.047198
LOC_28                        1.044135
LOC_29                        1.095148
LOC_30                        1.054410
LOC_31                        1.026741
LOC_32                        1.047077
LOC_33                        1.011737
LOC_34                        1.063960
LOC_35                        1.139519
LOC_37                        1.112600
LOC_38                        1.023370
LOC_39                        1.123051
LOC_40                        1.095179
LOC_41                        1.070114
LOC_42                        1.116521
LOC_44                        1.011471
LOC_46                        1.053036
LOC_47                        1.059537
LOC_48                        1.335241
LOC_49                        1.058759
LOC_50                        1.009143
LOC_51                        1.445043
LOC_53                        1.157121
LOC_55                        1.045923
TOA_15                        1.404428
TOA_20                        1.028182
TOA_30                        1.233026
TOA_32                        1.005917
TOA_35                        1.076566
TOA_38                        1.172315
TOA_40                        1.060716
TOA_42                        1.010484
TOA_44                        1.003670
PPGROUP_11                    1.155575

Removed AFTER this step: LOC_12 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8263  -0.7402  -0.1370   0.8187   3.2342  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.80540    0.32925   8.521  < 2e-16 ***
GSEGRD                        -0.50923    0.11971  -4.254 2.10e-05 ***
SalaryOverUnderIndAvg          0.19146    0.58208   0.329 0.742214    
LowerLimitAge                 -2.02002    0.11942 -16.915  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97114    0.10122   9.594  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14234    0.09472   1.503 0.132905    
BLS_FEDERAL_JobOpenings_Level  0.61367    0.06946   8.835  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03481    0.09839   0.354 0.723509    
LOSSqrt                       -6.61374    0.19337 -34.203  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20579    0.10559  -1.949 0.051304 .  
AGELVL_B                      -0.58419    0.21970  -2.659 0.007836 ** 
AGELVL_C                      -0.16713    0.08532  -1.959 0.050124 .  
AGELVL_E                       0.27408    0.06730   4.072 4.65e-05 ***
AGELVL_F                       0.49001    0.07012   6.988 2.78e-12 ***
AGELVL_G                       0.71342    0.07393   9.649  < 2e-16 ***
AGELVL_H                       0.84787    0.07620  11.127  < 2e-16 ***
AGELVL_I                       0.71878    0.08108   8.865  < 2e-16 ***
LOC_01                        -0.21317    0.16798  -1.269 0.204416    
LOC_02                         0.15154    0.25124   0.603 0.546379    
LOC_04                         0.52333    0.13802   3.792 0.000150 ***
LOC_05                         0.07797    0.23315   0.334 0.738069    
LOC_06                         0.40805    0.08966   4.551 5.33e-06 ***
LOC_08                         0.14082    0.12617   1.116 0.264380    
LOC_10                         0.19340    0.58112   0.333 0.739279    
LOC_13                         0.16614    0.11638   1.428 0.153396    
LOC_15                        -0.07155    0.16113  -0.444 0.657011    
LOC_17                        -0.22462    0.14468  -1.553 0.120534    
LOC_18                        -0.23096    0.25066  -0.921 0.356844    
LOC_19                         0.21160    0.31926   0.663 0.507483    
LOC_20                         0.60973    0.25582   2.383 0.017151 *  
LOC_22                        -0.11519    0.20752  -0.555 0.578834    
LOC_24                        -0.11200    0.08401  -1.333 0.182469    
LOC_25                         0.14098    0.18145   0.777 0.437158    
LOC_26                        -0.07502    0.20076  -0.374 0.708646    
LOC_27                         0.28476    0.23729   1.200 0.230117    
LOC_28                        -0.12887    0.24570  -0.524 0.599934    
LOC_29                        -0.35394    0.16919  -2.092 0.036442 *  
LOC_30                         0.67651    0.23981   2.821 0.004787 ** 
LOC_31                         0.12846    0.33434   0.384 0.700804    
LOC_32                         0.22791    0.24350   0.936 0.349273    
LOC_33                         0.19380    0.51809   0.374 0.708349    
LOC_34                        -0.18530    0.19522  -0.949 0.342520    
LOC_35                         0.46864    0.15157   3.092 0.001989 ** 
LOC_37                         0.06427    0.15248   0.421 0.673391    
LOC_38                         0.48449    0.36617   1.323 0.185796    
LOC_39                        -0.29426    0.14272  -2.062 0.039225 *  
LOC_40                         0.17429    0.16564   1.052 0.292679    
LOC_41                         0.11132    0.19660   0.566 0.571235    
LOC_42                        -0.29839    0.14359  -2.078 0.037704 *  
LOC_44                         0.33498    0.49123   0.682 0.495282    
LOC_46                         0.81038    0.25361   3.195 0.001396 ** 
LOC_47                        -0.18278    0.20965  -0.872 0.383283    
LOC_48                         0.24807    0.09359   2.651 0.008035 ** 
LOC_49                        -0.18879    0.20845  -0.906 0.365092    
LOC_50                        -0.41312    0.59259  -0.697 0.485708    
LOC_51                        -0.15868    0.07992  -1.986 0.047086 *  
LOC_53                         0.39433    0.12876   3.062 0.002196 ** 
LOC_55                        -0.32492    0.25001  -1.300 0.193720    
TOA_15                         0.06706    0.06908   0.971 0.331681    
TOA_20                         1.12749    0.19281   5.848 4.98e-09 ***
TOA_30                         0.34477    0.08846   3.898 9.71e-05 ***
TOA_32                         0.42041    0.75398   0.558 0.577123    
TOA_35                        -0.53827    0.28642  -1.879 0.060201 .  
TOA_38                         0.32826    0.06710   4.892 9.99e-07 ***
TOA_40                        -0.10680    0.17623  -0.606 0.544492    
TOA_42                         1.13831    0.44134   2.579 0.009903 ** 
TOA_44                         2.16131    1.07204   2.016 0.043792 *  
PPGROUP_11                    -0.41059    0.11705  -3.508 0.000452 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14852  degrees of freedom
AIC: 14680

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.607245
SalaryOverUnderIndAvg         1.335121
LowerLimitAge                 2.313769
BLS_FEDERAL_OtherSep_Rate     1.778650
BLS_FEDERAL_Quits_Rate        1.238726
BLS_FEDERAL_JobOpenings_Level 1.187870
BLS_FEDERAL_Layoffs_Rate      1.400016
LOSSqrt                       1.853161
SEPCount_EFDATE_OCCLog        1.106784
AGELVL_B                      1.165808
AGELVL_C                      1.661360
AGELVL_E                      1.406768
AGELVL_F                      1.282978
AGELVL_G                      1.268886
AGELVL_H                      1.359556
AGELVL_I                      1.515651
LOC_01                        1.070131
LOC_02                        1.038484
LOC_04                        1.142519
LOC_05                        1.039944
LOC_06                        1.303912
LOC_08                        1.124567
LOC_10                        1.007463
LOC_13                        1.155554
LOC_15                        1.080483
LOC_17                        1.086394
LOC_18                        1.034998
LOC_19                        1.021963
LOC_20                        1.035220
LOC_22                        1.047533
LOC_24                        1.319782
LOC_25                        1.052940
LOC_26                        1.052145
LOC_27                        1.037878
LOC_28                        1.034888
LOC_29                        1.075079
LOC_30                        1.044380
LOC_31                        1.021553
LOC_32                        1.037710
LOC_33                        1.009625
LOC_34                        1.052621
LOC_35                        1.112767
LOC_37                        1.089040
LOC_38                        1.019182
LOC_39                        1.096588
LOC_40                        1.075560
LOC_41                        1.055726
LOC_42                        1.092420
LOC_44                        1.009451
LOC_46                        1.043682
LOC_47                        1.047064
LOC_48                        1.265073
LOC_49                        1.046265
LOC_50                        1.007500
LOC_51                        1.355118
LOC_53                        1.125331
LOC_55                        1.037232
TOA_15                        1.404263
TOA_20                        1.028151
TOA_30                        1.226667
TOA_32                        1.005916
TOA_35                        1.076319
TOA_38                        1.170155
TOA_40                        1.060117
TOA_42                        1.010434
TOA_44                        1.003412
PPGROUP_11                    1.155529

Removed AFTER this step: SalaryOverUnderIndAvg 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8239  -0.7404  -0.1371   0.8187   3.2336  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89160    0.19957  14.489  < 2e-16 ***
GSEGRD                        -0.51799    0.11670  -4.439 9.05e-06 ***
LowerLimitAge                 -2.01372    0.11787 -17.084  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97124    0.10122   9.595  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14212    0.09471   1.501 0.133478    
BLS_FEDERAL_JobOpenings_Level  0.61375    0.06946   8.836  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03475    0.09839   0.353 0.723942    
LOSSqrt                       -6.59774    0.18710 -35.263  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20538    0.10558  -1.945 0.051740 .  
AGELVL_B                      -0.58240    0.21962  -2.652 0.008005 ** 
AGELVL_C                      -0.16672    0.08530  -1.954 0.050655 .  
AGELVL_E                       0.27446    0.06729   4.079 4.53e-05 ***
AGELVL_F                       0.49088    0.07007   7.006 2.45e-12 ***
AGELVL_G                       0.71429    0.07389   9.667  < 2e-16 ***
AGELVL_H                       0.84850    0.07618  11.138  < 2e-16 ***
AGELVL_I                       0.71936    0.08107   8.874  < 2e-16 ***
LOC_01                        -0.21657    0.16764  -1.292 0.196404    
LOC_02                         0.15481    0.25105   0.617 0.537466    
LOC_04                         0.51884    0.13733   3.778 0.000158 ***
LOC_05                         0.07475    0.23294   0.321 0.748273    
LOC_06                         0.41233    0.08871   4.648 3.35e-06 ***
LOC_08                         0.14002    0.12614   1.110 0.267008    
LOC_10                         0.19149    0.58147   0.329 0.741913    
LOC_13                         0.16316    0.11603   1.406 0.159665    
LOC_15                        -0.07020    0.16105  -0.436 0.662937    
LOC_17                        -0.22441    0.14469  -1.551 0.120896    
LOC_18                        -0.23329    0.25053  -0.931 0.351769    
LOC_19                         0.20836    0.31913   0.653 0.513823    
LOC_20                         0.60541    0.25542   2.370 0.017775 *  
LOC_22                        -0.11777    0.20737  -0.568 0.570091    
LOC_24                        -0.11076    0.08393  -1.320 0.186936    
LOC_25                         0.14289    0.18139   0.788 0.430843    
LOC_26                        -0.07515    0.20076  -0.374 0.708156    
LOC_27                         0.28459    0.23732   1.199 0.230457    
LOC_28                        -0.13289    0.24538  -0.542 0.588100    
LOC_29                        -0.35707    0.16892  -2.114 0.034530 *  
LOC_30                         0.67507    0.23964   2.817 0.004847 ** 
LOC_31                         0.12472    0.33413   0.373 0.708956    
LOC_32                         0.22599    0.24338   0.929 0.353131    
LOC_33                         0.19286    0.51803   0.372 0.709678    
LOC_34                        -0.18278    0.19513  -0.937 0.348902    
LOC_35                         0.46420    0.15097   3.075 0.002107 ** 
LOC_37                         0.06234    0.15237   0.409 0.682450    
LOC_38                         0.48236    0.36614   1.317 0.187698    
LOC_39                        -0.29586    0.14263  -2.074 0.038045 *  
LOC_40                         0.17175    0.16545   1.038 0.299249    
LOC_41                         0.10949    0.19652   0.557 0.577441    
LOC_42                        -0.29996    0.14352  -2.090 0.036619 *  
LOC_44                         0.33537    0.49119   0.683 0.494753    
LOC_46                         0.80724    0.25338   3.186 0.001443 ** 
LOC_47                        -0.18583    0.20941  -0.887 0.374877    
LOC_48                         0.24744    0.09357   2.645 0.008179 ** 
LOC_49                        -0.19134    0.20829  -0.919 0.358293    
LOC_50                        -0.41829    0.59224  -0.706 0.480007    
LOC_51                        -0.15853    0.07992  -1.984 0.047300 *  
LOC_53                         0.39482    0.12876   3.066 0.002167 ** 
LOC_55                        -0.32632    0.24993  -1.306 0.191664    
TOA_15                         0.06698    0.06907   0.970 0.332238    
TOA_20                         1.12599    0.19274   5.842 5.16e-09 ***
TOA_30                         0.34607    0.08838   3.916 9.01e-05 ***
TOA_32                         0.42125    0.75454   0.558 0.576652    
TOA_35                        -0.53787    0.28640  -1.878 0.060373 .  
TOA_38                         0.32954    0.06699   4.919 8.68e-07 ***
TOA_40                        -0.10399    0.17601  -0.591 0.554643    
TOA_42                         1.13918    0.44135   2.581 0.009847 ** 
TOA_44                         2.16322    1.07202   2.018 0.043602 *  
PPGROUP_11                    -0.40782    0.11671  -3.494 0.000475 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14853  degrees of freedom
AIC: 14678

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.527142
LowerLimitAge                 2.254026
BLS_FEDERAL_OtherSep_Rate     1.778706
BLS_FEDERAL_Quits_Rate        1.238709
BLS_FEDERAL_JobOpenings_Level 1.187876
BLS_FEDERAL_Layoffs_Rate      1.400012
LOSSqrt                       1.735002
SEPCount_EFDATE_OCCLog        1.106562
AGELVL_B                      1.165066
AGELVL_C                      1.661044
AGELVL_E                      1.406387
AGELVL_F                      1.281202
AGELVL_G                      1.267262
AGELVL_H                      1.358699
AGELVL_I                      1.514945
LOC_01                        1.066097
LOC_02                        1.036863
LOC_04                        1.131292
LOC_05                        1.038118
LOC_06                        1.276608
LOC_08                        1.124136
LOC_10                        1.007351
LOC_13                        1.148525
LOC_15                        1.079801
LOC_17                        1.086368
LOC_18                        1.034179
LOC_19                        1.020988
LOC_20                        1.032491
LOC_22                        1.046036
LOC_24                        1.316978
LOC_25                        1.051856
LOC_26                        1.052143
LOC_27                        1.037870
LOC_28                        1.032325
LOC_29                        1.071707
LOC_30                        1.044110
LOC_31                        1.020370
LOC_32                        1.037130
LOC_33                        1.009601
LOC_34                        1.050963
LOC_35                        1.103842
LOC_37                        1.087433
LOC_38                        1.018865
LOC_39                        1.095332
LOC_40                        1.073209
LOC_41                        1.054876
LOC_42                        1.091195
LOC_44                        1.009445
LOC_46                        1.042212
LOC_47                        1.045038
LOC_48                        1.264614
LOC_49                        1.044829
LOC_50                        1.006794
LOC_51                        1.355129
LOC_53                        1.125190
LOC_55                        1.036940
TOA_15                        1.404316
TOA_20                        1.027572
TOA_30                        1.224091
TOA_32                        1.005899
TOA_35                        1.076276
TOA_38                        1.166152
TOA_40                        1.057631
TOA_42                        1.010401
TOA_44                        1.003387
PPGROUP_11                    1.149157

Removed AFTER this step: LOC_05 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8240  -0.7405  -0.1370   0.8188   3.2331  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89574    0.19916  14.540  < 2e-16 ***
GSEGRD                        -0.51987    0.11655  -4.461 8.17e-06 ***
LowerLimitAge                 -2.01346    0.11787 -17.083  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97071    0.10121   9.591  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14231    0.09471   1.503 0.132951    
BLS_FEDERAL_JobOpenings_Level  0.61358    0.06946   8.834  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03495    0.09838   0.355 0.722413    
LOSSqrt                       -6.59714    0.18709 -35.263  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20562    0.10557  -1.948 0.051465 .  
AGELVL_B                      -0.58269    0.21961  -2.653 0.007971 ** 
AGELVL_C                      -0.16637    0.08530  -1.951 0.051113 .  
AGELVL_E                       0.27500    0.06727   4.088 4.35e-05 ***
AGELVL_F                       0.49150    0.07004   7.017 2.26e-12 ***
AGELVL_G                       0.71436    0.07389   9.668  < 2e-16 ***
AGELVL_H                       0.84888    0.07617  11.144  < 2e-16 ***
AGELVL_I                       0.71988    0.08105   8.882  < 2e-16 ***
LOC_01                        -0.21956    0.16739  -1.312 0.189626    
LOC_02                         0.15166    0.25086   0.605 0.545460    
LOC_04                         0.51568    0.13698   3.765 0.000167 ***
LOC_06                         0.40930    0.08821   4.640 3.48e-06 ***
LOC_08                         0.13707    0.12581   1.089 0.275945    
LOC_10                         0.18812    0.58139   0.324 0.746267    
LOC_13                         0.16020    0.11566   1.385 0.166028    
LOC_15                        -0.07321    0.16078  -0.455 0.648857    
LOC_17                        -0.22734    0.14440  -1.574 0.115408    
LOC_18                        -0.23650    0.25034  -0.945 0.344808    
LOC_19                         0.20504    0.31897   0.643 0.520348    
LOC_20                         0.60229    0.25523   2.360 0.018286 *  
LOC_22                        -0.12090    0.20715  -0.584 0.559457    
LOC_24                        -0.11347    0.08350  -1.359 0.174169    
LOC_25                         0.13998    0.18116   0.773 0.439703    
LOC_26                        -0.07833    0.20052  -0.391 0.696079    
LOC_27                         0.28133    0.23711   1.187 0.235417    
LOC_28                        -0.13601    0.24518  -0.555 0.579076    
LOC_29                        -0.36023    0.16863  -2.136 0.032665 *  
LOC_30                         0.67185    0.23943   2.806 0.005016 ** 
LOC_31                         0.12146    0.33398   0.364 0.716107    
LOC_32                         0.22275    0.24318   0.916 0.359676    
LOC_33                         0.18941    0.51792   0.366 0.714582    
LOC_34                        -0.18576    0.19491  -0.953 0.340568    
LOC_35                         0.46113    0.15067   3.060 0.002210 ** 
LOC_37                         0.05926    0.15206   0.390 0.696769    
LOC_38                         0.47912    0.36600   1.309 0.190515    
LOC_39                        -0.29894    0.14231  -2.101 0.035674 *  
LOC_40                         0.16868    0.16517   1.021 0.307150    
LOC_41                         0.10629    0.19626   0.542 0.588106    
LOC_42                        -0.30289    0.14324  -2.115 0.034462 *  
LOC_44                         0.33234    0.49109   0.677 0.498578    
LOC_46                         0.80397    0.25318   3.175 0.001496 ** 
LOC_47                        -0.18895    0.20919  -0.903 0.366378    
LOC_48                         0.24441    0.09309   2.626 0.008651 ** 
LOC_49                        -0.19447    0.20806  -0.935 0.349963    
LOC_50                        -0.42139    0.59218  -0.712 0.476716    
LOC_51                        -0.16137    0.07943  -2.032 0.042188 *  
LOC_53                         0.39176    0.12840   3.051 0.002281 ** 
LOC_55                        -0.32961    0.24972  -1.320 0.186867    
TOA_15                         0.06681    0.06907   0.967 0.333433    
TOA_20                         1.12560    0.19274   5.840 5.22e-09 ***
TOA_30                         0.34516    0.08833   3.907 9.33e-05 ***
TOA_32                         0.42162    0.75451   0.559 0.576300    
TOA_35                        -0.53911    0.28637  -1.883 0.059760 .  
TOA_38                         0.33030    0.06695   4.934 8.07e-07 ***
TOA_40                        -0.10477    0.17600  -0.595 0.551652    
TOA_42                         1.13833    0.44134   2.579 0.009901 ** 
TOA_44                         2.16082    1.07198   2.016 0.043829 *  
PPGROUP_11                    -0.40816    0.11671  -3.497 0.000470 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14854  degrees of freedom
AIC: 14676

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.523291
LowerLimitAge                 2.253889
BLS_FEDERAL_OtherSep_Rate     1.778212
BLS_FEDERAL_Quits_Rate        1.238671
BLS_FEDERAL_JobOpenings_Level 1.187809
BLS_FEDERAL_Layoffs_Rate      1.399937
LOSSqrt                       1.734824
SEPCount_EFDATE_OCCLog        1.106515
AGELVL_B                      1.165062
AGELVL_C                      1.660760
AGELVL_E                      1.405480
AGELVL_F                      1.280258
AGELVL_G                      1.267235
AGELVL_H                      1.358388
AGELVL_I                      1.514410
LOC_01                        1.062813
LOC_02                        1.035284
LOC_04                        1.125493
LOC_06                        1.262244
LOC_08                        1.118187
LOC_10                        1.007023
LOC_13                        1.141310
LOC_15                        1.076147
LOC_17                        1.082074
LOC_18                        1.032536
LOC_19                        1.019915
LOC_20                        1.031001
LOC_22                        1.043732
LOC_24                        1.303675
LOC_25                        1.049244
LOC_26                        1.049598
LOC_27                        1.035979
LOC_28                        1.030711
LOC_29                        1.068064
LOC_30                        1.042276
LOC_31                        1.019429
LOC_32                        1.035346
LOC_33                        1.009167
LOC_34                        1.048598
LOC_35                        1.099412
LOC_37                        1.083137
LOC_38                        1.018094
LOC_39                        1.090409
LOC_40                        1.069642
LOC_41                        1.052178
LOC_42                        1.086817
LOC_44                        1.009071
LOC_46                        1.040532
LOC_47                        1.042780
LOC_48                        1.251728
LOC_49                        1.042546
LOC_50                        1.006526
LOC_51                        1.338522
LOC_53                        1.119020
LOC_55                        1.035205
TOA_15                        1.404253
TOA_20                        1.027536
TOA_30                        1.222811
TOA_32                        1.005897
TOA_35                        1.076091
TOA_38                        1.164749
TOA_40                        1.057430
TOA_42                        1.010368
TOA_44                        1.003338
PPGROUP_11                    1.149065

Removed AFTER this step: LOC_10 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8240  -0.7404  -0.1370   0.8191   3.2328  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89689    0.19914  14.547  < 2e-16 ***
GSEGRD                        -0.52058    0.11653  -4.467 7.92e-06 ***
LowerLimitAge                 -2.01293    0.11785 -17.080  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.97068    0.10121   9.591  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.14230    0.09471   1.503 0.132955    
BLS_FEDERAL_JobOpenings_Level  0.61366    0.06945   8.835  < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate       0.03466    0.09838   0.352 0.724576    
LOSSqrt                       -6.59706    0.18709 -35.262  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20582    0.10558  -1.949 0.051240 .  
AGELVL_B                      -0.58310    0.21961  -2.655 0.007927 ** 
AGELVL_C                      -0.16636    0.08530  -1.950 0.051129 .  
AGELVL_E                       0.27507    0.06727   4.089 4.33e-05 ***
AGELVL_F                       0.49185    0.07003   7.023 2.17e-12 ***
AGELVL_G                       0.71431    0.07389   9.667  < 2e-16 ***
AGELVL_H                       0.84886    0.07617  11.144  < 2e-16 ***
AGELVL_I                       0.72001    0.08104   8.884  < 2e-16 ***
LOC_01                        -0.22071    0.16735  -1.319 0.187212    
LOC_02                         0.15036    0.25083   0.599 0.548876    
LOC_04                         0.51438    0.13692   3.757 0.000172 ***
LOC_06                         0.40811    0.08813   4.631 3.65e-06 ***
LOC_08                         0.13587    0.12576   1.080 0.279954    
LOC_13                         0.15904    0.11561   1.376 0.168925    
LOC_15                        -0.07442    0.16073  -0.463 0.643383    
LOC_17                        -0.22846    0.14436  -1.583 0.113517    
LOC_18                        -0.23784    0.25031  -0.950 0.342016    
LOC_19                         0.20371    0.31895   0.639 0.523018    
LOC_20                         0.60099    0.25520   2.355 0.018525 *  
LOC_22                        -0.12217    0.20711  -0.590 0.555276    
LOC_24                        -0.11455    0.08344  -1.373 0.169798    
LOC_25                         0.13885    0.18113   0.767 0.443333    
LOC_26                        -0.07960    0.20048  -0.397 0.691351    
LOC_27                         0.28003    0.23708   1.181 0.237543    
LOC_28                        -0.13729    0.24516  -0.560 0.575467    
LOC_29                        -0.36147    0.16859  -2.144 0.032027 *  
LOC_30                         0.67054    0.23940   2.801 0.005096 ** 
LOC_31                         0.12018    0.33396   0.360 0.718957    
LOC_32                         0.22136    0.24314   0.910 0.362604    
LOC_33                         0.18802    0.51791   0.363 0.716575    
LOC_34                        -0.18688    0.19488  -0.959 0.337576    
LOC_35                         0.45990    0.15062   3.053 0.002263 ** 
LOC_37                         0.05801    0.15202   0.382 0.702741    
LOC_38                         0.47780    0.36599   1.306 0.191718    
LOC_39                        -0.30014    0.14226  -2.110 0.034880 *  
LOC_40                         0.16746    0.16513   1.014 0.310531    
LOC_41                         0.10501    0.19623   0.535 0.592532    
LOC_42                        -0.30401    0.14319  -2.123 0.033751 *  
LOC_44                         0.33110    0.49107   0.674 0.500158    
LOC_46                         0.80264    0.25315   3.171 0.001521 ** 
LOC_47                        -0.19024    0.20915  -0.910 0.363034    
LOC_48                         0.24319    0.09301   2.615 0.008933 ** 
LOC_49                        -0.19573    0.20803  -0.941 0.346767    
LOC_50                        -0.42279    0.59217  -0.714 0.475245    
LOC_51                        -0.16251    0.07935  -2.048 0.040566 *  
LOC_53                         0.39056    0.12835   3.043 0.002342 ** 
LOC_55                        -0.33092    0.24969  -1.325 0.185072    
TOA_15                         0.06727    0.06906   0.974 0.329988    
TOA_20                         1.12544    0.19274   5.839 5.24e-09 ***
TOA_30                         0.34484    0.08833   3.904 9.45e-05 ***
TOA_32                         0.42181    0.75448   0.559 0.576109    
TOA_35                        -0.53935    0.28637  -1.883 0.059643 .  
TOA_38                         0.33079    0.06693   4.943 7.71e-07 ***
TOA_40                        -0.10493    0.17600  -0.596 0.551059    
TOA_42                         1.13809    0.44134   2.579 0.009916 ** 
TOA_44                         2.15990    1.07197   2.015 0.043917 *  
PPGROUP_11                    -0.40797    0.11671  -3.496 0.000473 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14855  degrees of freedom
AIC: 14674

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.522785
LowerLimitAge                 2.253353
BLS_FEDERAL_OtherSep_Rate     1.778210
BLS_FEDERAL_Quits_Rate        1.238664
BLS_FEDERAL_JobOpenings_Level 1.187809
BLS_FEDERAL_Layoffs_Rate      1.399811
LOSSqrt                       1.734807
SEPCount_EFDATE_OCCLog        1.106482
AGELVL_B                      1.165040
AGELVL_C                      1.660770
AGELVL_E                      1.405477
AGELVL_F                      1.280008
AGELVL_G                      1.267222
AGELVL_H                      1.358360
AGELVL_I                      1.514380
LOC_01                        1.062331
LOC_02                        1.035017
LOC_04                        1.124519
LOC_06                        1.260043
LOC_08                        1.117227
LOC_13                        1.140212
LOC_15                        1.075572
LOC_17                        1.081454
LOC_18                        1.032254
LOC_19                        1.019748
LOC_20                        1.030746
LOC_22                        1.043362
LOC_24                        1.301641
LOC_25                        1.048856
LOC_26                        1.049201
LOC_27                        1.035679
LOC_28                        1.030445
LOC_29                        1.067514
LOC_30                        1.041979
LOC_31                        1.019288
LOC_32                        1.035027
LOC_33                        1.009098
LOC_34                        1.048271
LOC_35                        1.098716
LOC_37                        1.082450
LOC_38                        1.017969
LOC_39                        1.089676
LOC_40                        1.069092
LOC_41                        1.051757
LOC_42                        1.086193
LOC_44                        1.009011
LOC_46                        1.040257
LOC_47                        1.042404
LOC_48                        1.249699
LOC_49                        1.042181
LOC_50                        1.006473
LOC_51                        1.335913
LOC_53                        1.118110
LOC_55                        1.034938
TOA_15                        1.403746
TOA_20                        1.027529
TOA_30                        1.222663
TOA_32                        1.005897
TOA_35                        1.076098
TOA_38                        1.164156
TOA_40                        1.057424
TOA_42                        1.010366
TOA_44                        1.003331
PPGROUP_11                    1.149045

Removed AFTER this step: BLS_FEDERAL_Layoffs_Rate 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8215  -0.7397  -0.1370   0.8186   3.2323  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89296    0.19883  14.550  < 2e-16 ***
GSEGRD                        -0.52011    0.11652  -4.464 8.05e-06 ***
LowerLimitAge                 -2.01244    0.11783 -17.079  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98838    0.08787  11.248  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13754    0.09375   1.467 0.142358    
BLS_FEDERAL_JobOpenings_Level  0.62068    0.06654   9.329  < 2e-16 ***
LOSSqrt                       -6.59432    0.18691 -35.280  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20450    0.10551  -1.938 0.052599 .  
AGELVL_B                      -0.58303    0.21964  -2.655 0.007942 ** 
AGELVL_C                      -0.16636    0.08530  -1.950 0.051139 .  
AGELVL_E                       0.27509    0.06727   4.089 4.33e-05 ***
AGELVL_F                       0.49141    0.07002   7.018 2.25e-12 ***
AGELVL_G                       0.71391    0.07388   9.663  < 2e-16 ***
AGELVL_H                       0.84829    0.07615  11.139  < 2e-16 ***
AGELVL_I                       0.71958    0.08104   8.880  < 2e-16 ***
LOC_01                        -0.22144    0.16739  -1.323 0.185864    
LOC_02                         0.14982    0.25078   0.597 0.550242    
LOC_04                         0.51394    0.13692   3.754 0.000174 ***
LOC_06                         0.40813    0.08813   4.631 3.64e-06 ***
LOC_08                         0.13539    0.12575   1.077 0.281616    
LOC_13                         0.15874    0.11560   1.373 0.169700    
LOC_15                        -0.07438    0.16074  -0.463 0.643577    
LOC_17                        -0.22882    0.14436  -1.585 0.112958    
LOC_18                        -0.23811    0.25033  -0.951 0.341512    
LOC_19                         0.20435    0.31900   0.641 0.521787    
LOC_20                         0.60233    0.25518   2.360 0.018252 *  
LOC_22                        -0.12154    0.20709  -0.587 0.557252    
LOC_24                        -0.11502    0.08343  -1.379 0.167991    
LOC_25                         0.13907    0.18110   0.768 0.442535    
LOC_26                        -0.07957    0.20050  -0.397 0.691462    
LOC_27                         0.27943    0.23701   1.179 0.238410    
LOC_28                        -0.13699    0.24513  -0.559 0.576264    
LOC_29                        -0.36064    0.16862  -2.139 0.032452 *  
LOC_30                         0.67078    0.23938   2.802 0.005076 ** 
LOC_31                         0.12062    0.33396   0.361 0.717960    
LOC_32                         0.22060    0.24313   0.907 0.364226    
LOC_33                         0.18743    0.51792   0.362 0.717435    
LOC_34                        -0.18590    0.19487  -0.954 0.340096    
LOC_35                         0.45979    0.15064   3.052 0.002271 ** 
LOC_37                         0.05776    0.15201   0.380 0.703957    
LOC_38                         0.47727    0.36604   1.304 0.192275    
LOC_39                        -0.29948    0.14224  -2.105 0.035259 *  
LOC_40                         0.16761    0.16510   1.015 0.310015    
LOC_41                         0.10539    0.19625   0.537 0.591258    
LOC_42                        -0.30455    0.14319  -2.127 0.033433 *  
LOC_44                         0.33139    0.49108   0.675 0.499788    
LOC_46                         0.80259    0.25318   3.170 0.001524 ** 
LOC_47                        -0.19143    0.20912  -0.915 0.359983    
LOC_48                         0.24338    0.09301   2.617 0.008877 ** 
LOC_49                        -0.19582    0.20801  -0.941 0.346499    
LOC_50                        -0.42553    0.59193  -0.719 0.472208    
LOC_51                        -0.16211    0.07934  -2.043 0.041028 *  
LOC_53                         0.39052    0.12834   3.043 0.002344 ** 
LOC_55                        -0.33047    0.24973  -1.323 0.185730    
TOA_15                         0.06750    0.06906   0.977 0.328362    
TOA_20                         1.12581    0.19272   5.842 5.17e-09 ***
TOA_30                         0.34470    0.08832   3.903 9.51e-05 ***
TOA_32                         0.41743    0.75428   0.553 0.579975    
TOA_35                        -0.53878    0.28639  -1.881 0.059937 .  
TOA_38                         0.33087    0.06693   4.944 7.66e-07 ***
TOA_40                        -0.10547    0.17599  -0.599 0.548978    
TOA_42                         1.13927    0.44140   2.581 0.009850 ** 
TOA_44                         2.15982    1.07198   2.015 0.043925 *  
PPGROUP_11                    -0.40745    0.11669  -3.492 0.000480 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14856  degrees of freedom
AIC: 14672

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.522526
LowerLimitAge                 2.252678
BLS_FEDERAL_OtherSep_Rate     1.341059
BLS_FEDERAL_Quits_Rate        1.213219
BLS_FEDERAL_JobOpenings_Level 1.090067
LOSSqrt                       1.731243
SEPCount_EFDATE_OCCLog        1.105098
AGELVL_B                      1.164971
AGELVL_C                      1.660784
AGELVL_E                      1.405500
AGELVL_F                      1.279617
AGELVL_G                      1.266914
AGELVL_H                      1.357709
AGELVL_I                      1.514000
LOC_01                        1.062121
LOC_02                        1.034990
LOC_04                        1.124417
LOC_06                        1.260015
LOC_08                        1.117082
LOC_13                        1.140145
LOC_15                        1.075570
LOC_17                        1.081393
LOC_18                        1.032244
LOC_19                        1.019707
LOC_20                        1.030516
LOC_22                        1.043295
LOC_24                        1.301291
LOC_25                        1.048858
LOC_26                        1.049178
LOC_27                        1.035641
LOC_28                        1.030433
LOC_29                        1.067263
LOC_30                        1.041969
LOC_31                        1.019269
LOC_32                        1.034950
LOC_33                        1.009094
LOC_34                        1.048058
LOC_35                        1.098709
LOC_37                        1.082421
LOC_38                        1.017943
LOC_39                        1.089478
LOC_40                        1.069107
LOC_41                        1.051718
LOC_42                        1.086052
LOC_44                        1.009007
LOC_46                        1.040244
LOC_47                        1.042137
LOC_48                        1.249661
LOC_49                        1.042196
LOC_50                        1.006303
LOC_51                        1.335636
LOC_53                        1.118108
LOC_55                        1.034902
TOA_15                        1.403673
TOA_20                        1.027479
TOA_30                        1.222618
TOA_32                        1.005622
TOA_35                        1.076003
TOA_38                        1.164145
TOA_40                        1.057360
TOA_42                        1.010300
TOA_44                        1.003330
PPGROUP_11                    1.148870

Removed AFTER this step: LOC_31 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8220  -0.7400  -0.1370   0.8187   3.2319  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89741    0.19845  14.600  < 2e-16 ***
GSEGRD                        -0.52216    0.11638  -4.487 7.24e-06 ***
LowerLimitAge                 -2.01181    0.11782 -17.075  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98810    0.08787  11.245  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13790    0.09374   1.471 0.141292    
BLS_FEDERAL_JobOpenings_Level  0.62026    0.06653   9.324  < 2e-16 ***
LOSSqrt                       -6.59406    0.18691 -35.279  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20555    0.10547  -1.949 0.051309 .  
AGELVL_B                      -0.58376    0.21963  -2.658 0.007863 ** 
AGELVL_C                      -0.16626    0.08530  -1.949 0.051278 .  
AGELVL_E                       0.27559    0.06726   4.098 4.18e-05 ***
AGELVL_F                       0.49156    0.07002   7.021 2.21e-12 ***
AGELVL_G                       0.71414    0.07388   9.667  < 2e-16 ***
AGELVL_H                       0.84817    0.07615  11.138  < 2e-16 ***
AGELVL_I                       0.71963    0.08104   8.880  < 2e-16 ***
LOC_01                        -0.22369    0.16727  -1.337 0.181139    
LOC_02                         0.14730    0.25069   0.588 0.556828    
LOC_04                         0.51149    0.13675   3.740 0.000184 ***
LOC_06                         0.40580    0.08790   4.617 3.90e-06 ***
LOC_08                         0.13308    0.12559   1.060 0.289301    
LOC_13                         0.15647    0.11543   1.356 0.175245    
LOC_15                        -0.07673    0.16061  -0.478 0.632847    
LOC_17                        -0.23104    0.14423  -1.602 0.109173    
LOC_18                        -0.24041    0.25025  -0.961 0.336709    
LOC_19                         0.20187    0.31892   0.633 0.526737    
LOC_20                         0.59991    0.25509   2.352 0.018684 *  
LOC_22                        -0.12401    0.20698  -0.599 0.549063    
LOC_24                        -0.11711    0.08323  -1.407 0.159397    
LOC_25                         0.13689    0.18100   0.756 0.449477    
LOC_26                        -0.08192    0.20039  -0.409 0.682684    
LOC_27                         0.27697    0.23691   1.169 0.242377    
LOC_28                        -0.13936    0.24503  -0.569 0.569528    
LOC_29                        -0.36303    0.16849  -2.155 0.031193 *  
LOC_30                         0.66815    0.23927   2.792 0.005230 ** 
LOC_32                         0.21814    0.24303   0.898 0.369400    
LOC_33                         0.18490    0.51789   0.357 0.721074    
LOC_34                        -0.18814    0.19478  -0.966 0.334096    
LOC_35                         0.45738    0.15049   3.039 0.002372 ** 
LOC_37                         0.05543    0.15187   0.365 0.715111    
LOC_38                         0.47450    0.36595   1.297 0.194762    
LOC_39                        -0.30177    0.14210  -2.124 0.033706 *  
LOC_40                         0.16525    0.16497   1.002 0.316483    
LOC_41                         0.10286    0.19613   0.524 0.599954    
LOC_42                        -0.30676    0.14306  -2.144 0.032016 *  
LOC_44                         0.32895    0.49101   0.670 0.502888    
LOC_46                         0.80000    0.25308   3.161 0.001572 ** 
LOC_47                        -0.19380    0.20902  -0.927 0.353817    
LOC_48                         0.24105    0.09279   2.598 0.009378 ** 
LOC_49                        -0.19822    0.20790  -0.953 0.340378    
LOC_50                        -0.42781    0.59187  -0.723 0.469796    
LOC_51                        -0.16431    0.07911  -2.077 0.037805 *  
LOC_53                         0.38812    0.12817   3.028 0.002460 ** 
LOC_55                        -0.33290    0.24964  -1.334 0.182360    
TOA_15                         0.06730    0.06905   0.975 0.329748    
TOA_20                         1.12623    0.19272   5.844 5.10e-09 ***
TOA_30                         0.34401    0.08830   3.896 9.78e-05 ***
TOA_32                         0.41739    0.75425   0.553 0.580001    
TOA_35                        -0.53999    0.28638  -1.886 0.059352 .  
TOA_38                         0.33071    0.06692   4.942 7.74e-07 ***
TOA_40                        -0.10610    0.17598  -0.603 0.546591    
TOA_42                         1.14124    0.44130   2.586 0.009707 ** 
TOA_44                         2.15830    1.07196   2.013 0.044072 *  
PPGROUP_11                    -0.40800    0.11667  -3.497 0.000471 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14544  on 14857  degrees of freedom
AIC: 14670

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.518890
LowerLimitAge                 2.252156
BLS_FEDERAL_OtherSep_Rate     1.340928
BLS_FEDERAL_Quits_Rate        1.213060
BLS_FEDERAL_JobOpenings_Level 1.089716
LOSSqrt                       1.731182
SEPCount_EFDATE_OCCLog        1.104275
AGELVL_B                      1.164884
AGELVL_C                      1.660730
AGELVL_E                      1.404908
AGELVL_F                      1.279585
AGELVL_G                      1.266846
AGELVL_H                      1.357674
AGELVL_I                      1.513987
LOC_01                        1.060657
LOC_02                        1.034190
LOC_04                        1.121670
LOC_06                        1.253316
LOC_08                        1.114198
LOC_13                        1.136800
LOC_15                        1.073821
LOC_17                        1.079438
LOC_18                        1.031580
LOC_19                        1.019240
LOC_20                        1.029806
LOC_22                        1.042166
LOC_24                        1.295076
LOC_25                        1.047698
LOC_26                        1.048083
LOC_27                        1.034792
LOC_28                        1.029699
LOC_29                        1.065633
LOC_30                        1.041013
LOC_32                        1.034146
LOC_33                        1.008909
LOC_34                        1.047016
LOC_35                        1.096568
LOC_37                        1.080494
LOC_38                        1.017498
LOC_39                        1.087323
LOC_40                        1.067453
LOC_41                        1.050386
LOC_42                        1.084089
LOC_44                        1.008818
LOC_46                        1.039417
LOC_47                        1.041114
LOC_48                        1.243719
LOC_49                        1.041140
LOC_50                        1.006190
LOC_51                        1.327837
LOC_53                        1.115132
LOC_55                        1.034160
TOA_15                        1.403614
TOA_20                        1.027442
TOA_30                        1.222028
TOA_32                        1.005622
TOA_35                        1.075881
TOA_38                        1.164063
TOA_40                        1.057257
TOA_42                        1.010168
TOA_44                        1.003315
PPGROUP_11                    1.148685

Removed AFTER this step: LOC_33 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8220  -0.7398  -0.1370   0.8188   3.2316  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89927    0.19839  14.614  < 2e-16 ***
GSEGRD                        -0.52315    0.11636  -4.496 6.92e-06 ***
LowerLimitAge                 -2.01141    0.11781 -17.074  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98788    0.08787  11.243  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13790    0.09374   1.471 0.141268    
BLS_FEDERAL_JobOpenings_Level  0.61989    0.06652   9.319  < 2e-16 ***
LOSSqrt                       -6.59373    0.18691 -35.278  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20566    0.10547  -1.950 0.051188 .  
AGELVL_B                      -0.58270    0.21961  -2.653 0.007970 ** 
AGELVL_C                      -0.16611    0.08530  -1.947 0.051483 .  
AGELVL_E                       0.27563    0.06726   4.098 4.16e-05 ***
AGELVL_F                       0.49160    0.07002   7.021 2.20e-12 ***
AGELVL_G                       0.71410    0.07387   9.666  < 2e-16 ***
AGELVL_H                       0.84816    0.07615  11.138  < 2e-16 ***
AGELVL_I                       0.71964    0.08103   8.881  < 2e-16 ***
LOC_01                        -0.22515    0.16723  -1.346 0.178176    
LOC_02                         0.14585    0.25066   0.582 0.560647    
LOC_04                         0.51002    0.13669   3.731 0.000190 ***
LOC_06                         0.40434    0.08780   4.605 4.12e-06 ***
LOC_08                         0.13172    0.12553   1.049 0.294036    
LOC_13                         0.15511    0.11537   1.345 0.178780    
LOC_15                        -0.07809    0.16057  -0.486 0.626705    
LOC_17                        -0.23244    0.14418  -1.612 0.106922    
LOC_18                        -0.24202    0.25021  -0.967 0.333402    
LOC_19                         0.20021    0.31890   0.628 0.530116    
LOC_20                         0.59843    0.25506   2.346 0.018962 *  
LOC_22                        -0.12550    0.20693  -0.606 0.544214    
LOC_24                        -0.11833    0.08316  -1.423 0.154763    
LOC_25                         0.13551    0.18096   0.749 0.453974    
LOC_26                        -0.08351    0.20034  -0.417 0.676794    
LOC_27                         0.27537    0.23688   1.163 0.245032    
LOC_28                        -0.14086    0.24500  -0.575 0.565327    
LOC_29                        -0.36455    0.16844  -2.164 0.030443 *  
LOC_30                         0.66662    0.23923   2.787 0.005327 ** 
LOC_32                         0.21649    0.24299   0.891 0.372941    
LOC_34                        -0.18957    0.19474  -0.973 0.330325    
LOC_35                         0.45594    0.15044   3.031 0.002439 ** 
LOC_37                         0.05396    0.15182   0.355 0.722253    
LOC_38                         0.47293    0.36593   1.292 0.196225    
LOC_39                        -0.30329    0.14204  -2.135 0.032743 *  
LOC_40                         0.16386    0.16492   0.994 0.320436    
LOC_41                         0.10130    0.19608   0.517 0.605409    
LOC_42                        -0.30823    0.14301  -2.155 0.031137 *  
LOC_44                         0.32742    0.49099   0.667 0.504864    
LOC_46                         0.79844    0.25304   3.155 0.001603 ** 
LOC_47                        -0.19536    0.20897  -0.935 0.349873    
LOC_48                         0.23963    0.09270   2.585 0.009738 ** 
LOC_49                        -0.19968    0.20786  -0.961 0.336738    
LOC_50                        -0.42940    0.59187  -0.726 0.468144    
LOC_51                        -0.16560    0.07903  -2.095 0.036129 *  
LOC_53                         0.38668    0.12810   3.018 0.002541 ** 
LOC_55                        -0.33461    0.24960  -1.341 0.180053    
TOA_15                         0.06712    0.06905   0.972 0.331057    
TOA_20                         1.12594    0.19272   5.842 5.14e-09 ***
TOA_30                         0.34358    0.08829   3.891 9.96e-05 ***
TOA_32                         0.41754    0.75422   0.554 0.579852    
TOA_35                        -0.54080    0.28637  -1.888 0.058963 .  
TOA_38                         0.33155    0.06688   4.957 7.15e-07 ***
TOA_40                        -0.10642    0.17598  -0.605 0.545364    
TOA_42                         1.14376    0.44121   2.592 0.009533 ** 
TOA_44                         2.15735    1.07195   2.013 0.044163 *  
PPGROUP_11                    -0.40792    0.11667  -3.496 0.000472 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14545  on 14858  degrees of freedom
AIC: 14669

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.518055
LowerLimitAge                 2.251755
BLS_FEDERAL_OtherSep_Rate     1.340828
BLS_FEDERAL_Quits_Rate        1.213043
BLS_FEDERAL_JobOpenings_Level 1.089487
LOSSqrt                       1.731028
SEPCount_EFDATE_OCCLog        1.104286
AGELVL_B                      1.164643
AGELVL_C                      1.660683
AGELVL_E                      1.404911
AGELVL_F                      1.279578
AGELVL_G                      1.266801
AGELVL_H                      1.357583
AGELVL_I                      1.513874
LOC_01                        1.060022
LOC_02                        1.033925
LOC_04                        1.120672
LOC_06                        1.250628
LOC_08                        1.113189
LOC_13                        1.135586
LOC_15                        1.073225
LOC_17                        1.078652
LOC_18                        1.031249
LOC_19                        1.019023
LOC_20                        1.029539
LOC_22                        1.041753
LOC_24                        1.292937
LOC_25                        1.047222
LOC_26                        1.047574
LOC_27                        1.034425
LOC_28                        1.029400
LOC_29                        1.064960
LOC_30                        1.040684
LOC_32                        1.033776
LOC_34                        1.046575
LOC_35                        1.095791
LOC_37                        1.079713
LOC_38                        1.017348
LOC_39                        1.086352
LOC_40                        1.066869
LOC_41                        1.049868
LOC_42                        1.083202
LOC_44                        1.008740
LOC_46                        1.039109
LOC_47                        1.040667
LOC_48                        1.241450
LOC_49                        1.040743
LOC_50                        1.006133
LOC_51                        1.325094
LOC_53                        1.114044
LOC_55                        1.033783
TOA_15                        1.403502
TOA_20                        1.027424
TOA_30                        1.221805
TOA_32                        1.005622
TOA_35                        1.075805
TOA_38                        1.162650
TOA_40                        1.057228
TOA_42                        1.009962
TOA_44                        1.003309
PPGROUP_11                    1.148697

Removed AFTER this step: LOC_37 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8219  -0.7402  -0.1370   0.8190   3.2308  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.90436    0.19789  14.677  < 2e-16 ***
GSEGRD                        -0.52571    0.11614  -4.526 6.00e-06 ***
LowerLimitAge                 -2.01068    0.11779 -17.070  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98784    0.08787  11.242  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13891    0.09370   1.483 0.138197    
BLS_FEDERAL_JobOpenings_Level  0.61971    0.06652   9.317  < 2e-16 ***
LOSSqrt                       -6.59449    0.18690 -35.284  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20430    0.10540  -1.938 0.052585 .  
AGELVL_B                      -0.58358    0.21959  -2.658 0.007869 ** 
AGELVL_C                      -0.16652    0.08529  -1.952 0.050888 .  
AGELVL_E                       0.27581    0.06725   4.101 4.11e-05 ***
AGELVL_F                       0.49169    0.07001   7.023 2.17e-12 ***
AGELVL_G                       0.71407    0.07387   9.666  < 2e-16 ***
AGELVL_H                       0.84841    0.07615  11.142  < 2e-16 ***
AGELVL_I                       0.71954    0.08104   8.879  < 2e-16 ***
LOC_01                        -0.22978    0.16672  -1.378 0.168137    
LOC_02                         0.14082    0.25027   0.563 0.573657    
LOC_04                         0.50496    0.13594   3.714 0.000204 ***
LOC_06                         0.39963    0.08680   4.604 4.15e-06 ***
LOC_08                         0.12696    0.12482   1.017 0.309097    
LOC_13                         0.15040    0.11461   1.312 0.189409    
LOC_15                        -0.08299    0.15998  -0.519 0.603908    
LOC_17                        -0.23707    0.14359  -1.651 0.098747 .  
LOC_18                        -0.24682    0.24985  -0.988 0.323204    
LOC_19                         0.19540    0.31861   0.613 0.539686    
LOC_20                         0.59348    0.25469   2.330 0.019796 *  
LOC_22                        -0.13032    0.20649  -0.631 0.527974    
LOC_24                        -0.12267    0.08226  -1.491 0.135896    
LOC_25                         0.13102    0.18053   0.726 0.467989    
LOC_26                        -0.08841    0.19988  -0.442 0.658247    
LOC_27                         0.27043    0.23648   1.144 0.252801    
LOC_28                        -0.14567    0.24464  -0.595 0.551549    
LOC_29                        -0.36941    0.16789  -2.200 0.027785 *  
LOC_30                         0.66155    0.23881   2.770 0.005602 ** 
LOC_32                         0.21169    0.24262   0.873 0.382920    
LOC_34                        -0.19411    0.19433  -0.999 0.317853    
LOC_35                         0.45093    0.14978   3.011 0.002607 ** 
LOC_38                         0.46797    0.36569   1.280 0.200659    
LOC_39                        -0.30799    0.14143  -2.178 0.029430 *  
LOC_40                         0.15903    0.16436   0.968 0.333270    
LOC_41                         0.09655    0.19562   0.494 0.621625    
LOC_42                        -0.31280    0.14243  -2.196 0.028076 *  
LOC_44                         0.32262    0.49077   0.657 0.510948    
LOC_46                         0.79307    0.25260   3.140 0.001691 ** 
LOC_47                        -0.20019    0.20854  -0.960 0.337058    
LOC_48                         0.23475    0.09168   2.560 0.010453 *  
LOC_49                        -0.20443    0.20743  -0.986 0.324364    
LOC_50                        -0.43426    0.59173  -0.734 0.463020    
LOC_51                        -0.17017    0.07799  -2.182 0.029112 *  
LOC_53                         0.38189    0.12740   2.998 0.002721 ** 
LOC_55                        -0.33944    0.24924  -1.362 0.173222    
TOA_15                         0.06689    0.06904   0.969 0.332684    
TOA_20                         1.12504    0.19271   5.838 5.28e-09 ***
TOA_30                         0.34171    0.08814   3.877 0.000106 ***
TOA_32                         0.41785    0.75419   0.554 0.579553    
TOA_35                        -0.54120    0.28633  -1.890 0.058745 .  
TOA_38                         0.33160    0.06688   4.958 7.12e-07 ***
TOA_40                        -0.10748    0.17597  -0.611 0.541348    
TOA_42                         1.14220    0.44119   2.589 0.009628 ** 
TOA_44                         2.15351    1.07191   2.009 0.044533 *  
PPGROUP_11                    -0.40806    0.11667  -3.497 0.000470 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14545  on 14859  degrees of freedom
AIC: 14667

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.512280
LowerLimitAge                 2.251018
BLS_FEDERAL_OtherSep_Rate     1.340790
BLS_FEDERAL_Quits_Rate        1.211930
BLS_FEDERAL_JobOpenings_Level 1.089395
LOSSqrt                       1.730883
SEPCount_EFDATE_OCCLog        1.102851
AGELVL_B                      1.164450
AGELVL_C                      1.660398
AGELVL_E                      1.404859
AGELVL_F                      1.279586
AGELVL_G                      1.266799
AGELVL_H                      1.357462
AGELVL_I                      1.513758
LOC_01                        1.053598
LOC_02                        1.030629
LOC_04                        1.108536
LOC_06                        1.222225
LOC_08                        1.100523
LOC_13                        1.120631
LOC_15                        1.065337
LOC_17                        1.069896
LOC_18                        1.028260
LOC_19                        1.017193
LOC_20                        1.026470
LOC_22                        1.037291
LOC_24                        1.265115
LOC_25                        1.042141
LOC_26                        1.042619
LOC_27                        1.030876
LOC_28                        1.026272
LOC_29                        1.057965
LOC_30                        1.036981
LOC_32                        1.030589
LOC_34                        1.042095
LOC_35                        1.086186
LOC_38                        1.015875
LOC_39                        1.076969
LOC_40                        1.059639
LOC_41                        1.045000
LOC_42                        1.074458
LOC_44                        1.007978
LOC_46                        1.035415
LOC_47                        1.036268
LOC_48                        1.214284
LOC_49                        1.036447
LOC_50                        1.005599
LOC_51                        1.290264
LOC_53                        1.101776
LOC_55                        1.030730
TOA_15                        1.403390
TOA_20                        1.027235
TOA_30                        1.217459
TOA_32                        1.005620
TOA_35                        1.075770
TOA_38                        1.162608
TOA_40                        1.056915
TOA_42                        1.009867
TOA_44                        1.003207
PPGROUP_11                    1.148730

Removed AFTER this step: LOC_26 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8217  -0.7400  -0.1370   0.8186   3.2318  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89972    0.19760  14.675  < 2e-16 ***
GSEGRD                        -0.52269    0.11594  -4.508 6.54e-06 ***
LowerLimitAge                 -2.01122    0.11778 -17.075  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98812    0.08787  11.246  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13821    0.09368   1.475 0.140120    
BLS_FEDERAL_JobOpenings_Level  0.61974    0.06652   9.317  < 2e-16 ***
LOSSqrt                       -6.59543    0.18689 -35.291  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20457    0.10540  -1.941 0.052270 .  
AGELVL_B                      -0.58386    0.21956  -2.659 0.007833 ** 
AGELVL_C                      -0.16654    0.08529  -1.953 0.050862 .  
AGELVL_E                       0.27579    0.06725   4.101 4.12e-05 ***
AGELVL_F                       0.49161    0.07001   7.022 2.19e-12 ***
AGELVL_G                       0.71401    0.07387   9.665  < 2e-16 ***
AGELVL_H                       0.84836    0.07615  11.141  < 2e-16 ***
AGELVL_I                       0.71947    0.08103   8.879  < 2e-16 ***
LOC_01                        -0.22580    0.16648  -1.356 0.174978    
LOC_02                         0.14502    0.25009   0.580 0.562009    
LOC_04                         0.50911    0.13562   3.754 0.000174 ***
LOC_06                         0.40370    0.08631   4.677 2.91e-06 ***
LOC_08                         0.13088    0.12450   1.051 0.293148    
LOC_13                         0.15427    0.11427   1.350 0.177010    
LOC_15                        -0.07906    0.15973  -0.495 0.620644    
LOC_17                        -0.23311    0.14331  -1.627 0.103821    
LOC_18                        -0.24254    0.24966  -0.971 0.331311    
LOC_19                         0.19973    0.31845   0.627 0.530541    
LOC_20                         0.59766    0.25451   2.348 0.018863 *  
LOC_22                        -0.12618    0.20628  -0.612 0.540744    
LOC_24                        -0.11914    0.08187  -1.455 0.145598    
LOC_25                         0.13490    0.18031   0.748 0.454376    
LOC_27                         0.27477    0.23627   1.163 0.244841    
LOC_28                        -0.14149    0.24445  -0.579 0.562720    
LOC_29                        -0.36522    0.16762  -2.179 0.029340 *  
LOC_30                         0.66586    0.23862   2.791 0.005262 ** 
LOC_32                         0.21598    0.24242   0.891 0.372966    
LOC_34                        -0.19008    0.19410  -0.979 0.327440    
LOC_35                         0.45509    0.14948   3.044 0.002331 ** 
LOC_38                         0.47226    0.36556   1.292 0.196393    
LOC_39                        -0.30391    0.14113  -2.153 0.031284 *  
LOC_40                         0.16304    0.16411   0.994 0.320464    
LOC_41                         0.10077    0.19539   0.516 0.606028    
LOC_42                        -0.30886    0.14215  -2.173 0.029795 *  
LOC_44                         0.32679    0.49071   0.666 0.505439    
LOC_46                         0.79751    0.25240   3.160 0.001579 ** 
LOC_47                        -0.19601    0.20832  -0.941 0.346751    
LOC_48                         0.23881    0.09122   2.618 0.008847 ** 
LOC_49                        -0.20040    0.20723  -0.967 0.333530    
LOC_50                        -0.43011    0.59163  -0.727 0.467239    
LOC_51                        -0.16640    0.07752  -2.147 0.031822 *  
LOC_53                         0.38596    0.12707   3.037 0.002386 ** 
LOC_55                        -0.33503    0.24903  -1.345 0.178522    
TOA_15                         0.06725    0.06904   0.974 0.330012    
TOA_20                         1.12544    0.19271   5.840 5.22e-09 ***
TOA_30                         0.34239    0.08812   3.885 0.000102 ***
TOA_32                         0.41734    0.75425   0.553 0.580046    
TOA_35                        -0.54545    0.28624  -1.906 0.056705 .  
TOA_38                         0.33055    0.06683   4.946 7.58e-07 ***
TOA_40                        -0.10744    0.17596  -0.611 0.541443    
TOA_42                         1.14324    0.44118   2.591 0.009560 ** 
TOA_44                         2.15621    1.07189   2.012 0.044263 *  
PPGROUP_11                    -0.40815    0.11668  -3.498 0.000469 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14545  on 14860  degrees of freedom
AIC: 14665

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.506995
LowerLimitAge                 2.250918
BLS_FEDERAL_OtherSep_Rate     1.340706
BLS_FEDERAL_Quits_Rate        1.211608
BLS_FEDERAL_JobOpenings_Level 1.089375
LOSSqrt                       1.730760
SEPCount_EFDATE_OCCLog        1.102818
AGELVL_B                      1.164402
AGELVL_C                      1.660390
AGELVL_E                      1.404808
AGELVL_F                      1.279548
AGELVL_G                      1.266786
AGELVL_H                      1.357452
AGELVL_I                      1.513875
LOC_01                        1.050524
LOC_02                        1.029147
LOC_04                        1.103224
LOC_06                        1.208461
LOC_08                        1.094939
LOC_13                        1.114088
LOC_15                        1.062019
LOC_17                        1.065725
LOC_18                        1.026702
LOC_19                        1.016233
LOC_20                        1.025059
LOC_22                        1.035150
LOC_24                        1.253144
LOC_25                        1.039673
LOC_27                        1.029097
LOC_28                        1.024737
LOC_29                        1.054584
LOC_30                        1.035252
LOC_32                        1.028939
LOC_34                        1.039802
LOC_35                        1.081890
LOC_38                        1.015156
LOC_39                        1.072350
LOC_40                        1.056385
LOC_41                        1.042503
LOC_42                        1.070213
LOC_44                        1.007605
LOC_46                        1.033782
LOC_47                        1.034125
LOC_48                        1.202093
LOC_49                        1.034435
LOC_50                        1.005344
LOC_51                        1.274819
LOC_53                        1.096022
LOC_55                        1.029062
TOA_15                        1.403215
TOA_20                        1.027209
TOA_30                        1.217094
TOA_32                        1.005618
TOA_35                        1.074343
TOA_38                        1.161137
TOA_40                        1.056920
TOA_42                        1.009835
TOA_44                        1.003174
PPGROUP_11                    1.148692

Removed AFTER this step: LOC_15 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8213  -0.7403  -0.1371   0.8185   3.2324  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89185    0.19694  14.684  < 2e-16 ***
GSEGRD                        -0.51991    0.11579  -4.490 7.12e-06 ***
LowerLimitAge                 -2.01176    0.11778 -17.081  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98863    0.08786  11.252  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13801    0.09368   1.473 0.140715    
BLS_FEDERAL_JobOpenings_Level  0.61995    0.06651   9.321  < 2e-16 ***
LOSSqrt                       -6.59402    0.18685 -35.290  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20629    0.10535  -1.958 0.050212 .  
AGELVL_B                      -0.58202    0.21953  -2.651 0.008020 ** 
AGELVL_C                      -0.16647    0.08528  -1.952 0.050948 .  
AGELVL_E                       0.27544    0.06725   4.096 4.21e-05 ***
AGELVL_F                       0.49183    0.07001   7.025 2.14e-12 ***
AGELVL_G                       0.71384    0.07388   9.663  < 2e-16 ***
AGELVL_H                       0.84800    0.07615  11.136  < 2e-16 ***
AGELVL_I                       0.71870    0.08102   8.871  < 2e-16 ***
LOC_01                        -0.22053    0.16613  -1.327 0.184354    
LOC_02                         0.15088    0.24979   0.604 0.545827    
LOC_04                         0.51511    0.13508   3.813 0.000137 ***
LOC_06                         0.40903    0.08563   4.777 1.78e-06 ***
LOC_08                         0.13645    0.12398   1.101 0.271090    
LOC_13                         0.15983    0.11371   1.406 0.159850    
LOC_17                        -0.22781    0.14291  -1.594 0.110919    
LOC_18                        -0.23745    0.24944  -0.952 0.341129    
LOC_19                         0.20496    0.31829   0.644 0.519609    
LOC_20                         0.60345    0.25423   2.374 0.017613 *  
LOC_22                        -0.12064    0.20597  -0.586 0.558060    
LOC_24                        -0.11396    0.08119  -1.404 0.160463    
LOC_25                         0.14000    0.18001   0.778 0.436721    
LOC_27                         0.28026    0.23600   1.188 0.235025    
LOC_28                        -0.13605    0.24419  -0.557 0.577423    
LOC_29                        -0.35965    0.16723  -2.151 0.031510 *  
LOC_30                         0.67183    0.23830   2.819 0.004813 ** 
LOC_32                         0.22121    0.24218   0.913 0.361039    
LOC_34                        -0.18493    0.19382  -0.954 0.340026    
LOC_35                         0.46102    0.14899   3.094 0.001973 ** 
LOC_38                         0.47806    0.36534   1.309 0.190696    
LOC_39                        -0.29862    0.14072  -2.122 0.033830 *  
LOC_40                         0.16876    0.16370   1.031 0.302600    
LOC_41                         0.10611    0.19509   0.544 0.586503    
LOC_42                        -0.30368    0.14176  -2.142 0.032177 *  
LOC_44                         0.33214    0.49062   0.677 0.498420    
LOC_46                         0.80366    0.25208   3.188 0.001432 ** 
LOC_47                        -0.19061    0.20803  -0.916 0.359538    
LOC_48                         0.24447    0.09050   2.701 0.006905 ** 
LOC_49                        -0.19481    0.20692  -0.941 0.346453    
LOC_50                        -0.42475    0.59154  -0.718 0.472731    
LOC_51                        -0.16096    0.07673  -2.098 0.035918 *  
LOC_53                         0.39156    0.12656   3.094 0.001975 ** 
LOC_55                        -0.32985    0.24881  -1.326 0.184929    
TOA_15                         0.06720    0.06904   0.973 0.330409    
TOA_20                         1.12515    0.19269   5.839 5.25e-09 ***
TOA_30                         0.34482    0.08798   3.919 8.88e-05 ***
TOA_32                         0.41696    0.75430   0.553 0.580413    
TOA_35                        -0.54259    0.28616  -1.896 0.057951 .  
TOA_38                         0.33198    0.06677   4.972 6.62e-07 ***
TOA_40                        -0.10507    0.17588  -0.597 0.550252    
TOA_42                         1.14433    0.44119   2.594 0.009494 ** 
TOA_44                         2.16068    1.07185   2.016 0.043817 *  
PPGROUP_11                    -0.40678    0.11665  -3.487 0.000488 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14545  on 14861  degrees of freedom
AIC: 14663

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.503479
LowerLimitAge                 2.250805
BLS_FEDERAL_OtherSep_Rate     1.340598
BLS_FEDERAL_Quits_Rate        1.211633
BLS_FEDERAL_JobOpenings_Level 1.089331
LOSSqrt                       1.730392
SEPCount_EFDATE_OCCLog        1.101626
AGELVL_B                      1.163997
AGELVL_C                      1.660373
AGELVL_E                      1.404599
AGELVL_F                      1.279487
AGELVL_G                      1.266678
AGELVL_H                      1.357266
AGELVL_I                      1.513259
LOC_01                        1.046210
LOC_02                        1.026840
LOC_04                        1.094413
LOC_06                        1.189597
LOC_08                        1.085976
LOC_13                        1.103284
LOC_17                        1.059736
LOC_18                        1.024950
LOC_19                        1.015103
LOC_20                        1.022888
LOC_22                        1.032090
LOC_24                        1.232548
LOC_25                        1.036251
LOC_27                        1.026820
LOC_28                        1.022660
LOC_29                        1.049793
LOC_30                        1.032611
LOC_32                        1.026972
LOC_34                        1.036780
LOC_35                        1.074937
LOC_38                        1.014109
LOC_39                        1.066168
LOC_40                        1.051144
LOC_41                        1.039316
LOC_42                        1.064367
LOC_44                        1.007114
LOC_46                        1.031278
LOC_47                        1.031270
LOC_48                        1.183171
LOC_49                        1.031354
LOC_50                        1.005006
LOC_51                        1.249082
LOC_53                        1.087281
LOC_55                        1.027229
TOA_15                        1.403272
TOA_20                        1.027213
TOA_30                        1.213379
TOA_32                        1.005617
TOA_35                        1.073867
TOA_38                        1.158998
TOA_40                        1.056125
TOA_42                        1.009808
TOA_44                        1.003102
PPGROUP_11                    1.148037

Removed AFTER this step: LOC_41 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8220  -0.7401  -0.1371   0.8186   3.2315  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.90074    0.19628  14.779  < 2e-16 ***
GSEGRD                        -0.52371    0.11558  -4.531 5.87e-06 ***
LowerLimitAge                 -2.01103    0.11776 -17.077  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98848    0.08786  11.251  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13821    0.09368   1.475 0.140131    
BLS_FEDERAL_JobOpenings_Level  0.62000    0.06651   9.322  < 2e-16 ***
LOSSqrt                       -6.59255    0.18684 -35.285  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20921    0.10522  -1.988 0.046767 *  
AGELVL_B                      -0.58245    0.21956  -2.653 0.007983 ** 
AGELVL_C                      -0.16714    0.08528  -1.960 0.049995 *  
AGELVL_E                       0.27528    0.06725   4.093 4.25e-05 ***
AGELVL_F                       0.49207    0.07000   7.029 2.08e-12 ***
AGELVL_G                       0.71398    0.07387   9.665  < 2e-16 ***
AGELVL_H                       0.84807    0.07614  11.139  < 2e-16 ***
AGELVL_I                       0.71903    0.08101   8.875  < 2e-16 ***
LOC_01                        -0.22506    0.16592  -1.356 0.174963    
LOC_02                         0.14582    0.24960   0.584 0.559077    
LOC_04                         0.51035    0.13478   3.786 0.000153 ***
LOC_06                         0.40440    0.08521   4.746 2.07e-06 ***
LOC_08                         0.13193    0.12370   1.067 0.286191    
LOC_13                         0.15536    0.11341   1.370 0.170716    
LOC_17                        -0.23223    0.14267  -1.628 0.103590    
LOC_18                        -0.24205    0.24928  -0.971 0.331549    
LOC_19                         0.19989    0.31813   0.628 0.529802    
LOC_20                         0.59867    0.25405   2.356 0.018449 *  
LOC_22                        -0.12544    0.20577  -0.610 0.542131    
LOC_24                        -0.11806    0.08084  -1.460 0.144168    
LOC_25                         0.13559    0.17982   0.754 0.450835    
LOC_27                         0.27530    0.23582   1.167 0.243049    
LOC_28                        -0.14090    0.24401  -0.577 0.563631    
LOC_29                        -0.36435    0.16700  -2.182 0.029132 *  
LOC_30                         0.66663    0.23808   2.800 0.005111 ** 
LOC_32                         0.21620    0.24200   0.893 0.371652    
LOC_34                        -0.18934    0.19365  -0.978 0.328182    
LOC_35                         0.45639    0.14874   3.068 0.002153 ** 
LOC_38                         0.47268    0.36517   1.294 0.195518    
LOC_39                        -0.30318    0.14046  -2.158 0.030893 *  
LOC_40                         0.16419    0.16348   1.004 0.315197    
LOC_42                        -0.30795    0.14154  -2.176 0.029572 *  
LOC_44                         0.32724    0.49048   0.667 0.504659    
LOC_46                         0.79866    0.25189   3.171 0.001521 ** 
LOC_47                        -0.19536    0.20784  -0.940 0.347238    
LOC_48                         0.23990    0.09010   2.662 0.007757 ** 
LOC_49                        -0.19952    0.20673  -0.965 0.334480    
LOC_50                        -0.42937    0.59145  -0.726 0.467864    
LOC_51                        -0.16527    0.07632  -2.166 0.030344 *  
LOC_53                         0.38687    0.12625   3.064 0.002183 ** 
LOC_55                        -0.33475    0.24863  -1.346 0.178182    
TOA_15                         0.06674    0.06903   0.967 0.333643    
TOA_20                         1.12448    0.19269   5.836 5.36e-09 ***
TOA_30                         0.34400    0.08796   3.911 9.19e-05 ***
TOA_32                         0.41707    0.75419   0.553 0.580263    
TOA_35                        -0.54416    0.28616  -1.902 0.057225 .  
TOA_38                         0.33213    0.06677   4.974 6.56e-07 ***
TOA_40                        -0.10608    0.17588  -0.603 0.546433    
TOA_42                         1.14546    0.44115   2.597 0.009416 ** 
TOA_44                         2.15741    1.07182   2.013 0.044131 *  
PPGROUP_11                    -0.40770    0.11663  -3.496 0.000473 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14545  on 14862  degrees of freedom
AIC: 14661

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.497918
LowerLimitAge                 2.250272
BLS_FEDERAL_OtherSep_Rate     1.340597
BLS_FEDERAL_Quits_Rate        1.211599
BLS_FEDERAL_JobOpenings_Level 1.089337
LOSSqrt                       1.729903
SEPCount_EFDATE_OCCLog        1.098816
AGELVL_B                      1.164069
AGELVL_C                      1.660025
AGELVL_E                      1.404572
AGELVL_F                      1.279456
AGELVL_G                      1.266619
AGELVL_H                      1.357234
AGELVL_I                      1.512974
LOC_01                        1.043598
LOC_02                        1.025422
LOC_04                        1.089839
LOC_06                        1.177903
LOC_08                        1.081119
LOC_13                        1.097553
LOC_17                        1.056338
LOC_18                        1.023783
LOC_19                        1.014235
LOC_20                        1.021675
LOC_22                        1.030213
LOC_24                        1.221963
LOC_25                        1.034158
LOC_27                        1.025298
LOC_28                        1.021304
LOC_29                        1.047001
LOC_30                        1.030953
LOC_32                        1.025495
LOC_34                        1.034977
LOC_35                        1.071456
LOC_38                        1.013371
LOC_39                        1.062398
LOC_40                        1.048404
LOC_42                        1.061124
LOC_44                        1.006775
LOC_46                        1.029916
LOC_47                        1.029458
LOC_48                        1.173022
LOC_49                        1.029562
LOC_50                        1.004802
LOC_51                        1.235817
LOC_53                        1.082251
LOC_55                        1.025900
TOA_15                        1.402979
TOA_20                        1.027189
TOA_30                        1.212992
TOA_32                        1.005617
TOA_35                        1.073894
TOA_38                        1.158938
TOA_40                        1.056016
TOA_42                        1.009783
TOA_44                        1.003071
PPGROUP_11                    1.147824

Removed AFTER this step: TOA_32 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8217  -0.7405  -0.1372   0.8185   3.2315  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89955    0.19626  14.774  < 2e-16 ***
GSEGRD                        -0.52186    0.11553  -4.517 6.27e-06 ***
LowerLimitAge                 -2.01069    0.11776 -17.074  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98851    0.08786  11.251  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13856    0.09368   1.479 0.139092    
BLS_FEDERAL_JobOpenings_Level  0.61953    0.06651   9.315  < 2e-16 ***
LOSSqrt                       -6.59306    0.18683 -35.288  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20988    0.10521  -1.995 0.046059 *  
AGELVL_B                      -0.58206    0.21956  -2.651 0.008023 ** 
AGELVL_C                      -0.16729    0.08527  -1.962 0.049784 *  
AGELVL_E                       0.27558    0.06725   4.098 4.17e-05 ***
AGELVL_F                       0.49182    0.07000   7.026 2.13e-12 ***
AGELVL_G                       0.71483    0.07386   9.678  < 2e-16 ***
AGELVL_H                       0.84764    0.07613  11.134  < 2e-16 ***
AGELVL_I                       0.71873    0.08101   8.872  < 2e-16 ***
LOC_01                        -0.22505    0.16591  -1.356 0.174951    
LOC_02                         0.14599    0.24959   0.585 0.558601    
LOC_04                         0.51059    0.13478   3.788 0.000152 ***
LOC_06                         0.40559    0.08518   4.762 1.92e-06 ***
LOC_08                         0.13195    0.12369   1.067 0.286101    
LOC_13                         0.15538    0.11341   1.370 0.170669    
LOC_17                        -0.23218    0.14267  -1.627 0.103648    
LOC_18                        -0.24187    0.24927  -0.970 0.331902    
LOC_19                         0.19998    0.31812   0.629 0.529604    
LOC_20                         0.59885    0.25405   2.357 0.018411 *  
LOC_22                        -0.12532    0.20576  -0.609 0.542506    
LOC_24                        -0.11821    0.08084  -1.462 0.143666    
LOC_25                         0.13565    0.17981   0.754 0.450602    
LOC_27                         0.27552    0.23581   1.168 0.242662    
LOC_28                        -0.13929    0.24412  -0.571 0.568282    
LOC_29                        -0.36418    0.16700  -2.181 0.029199 *  
LOC_30                         0.66684    0.23807   2.801 0.005094 ** 
LOC_32                         0.21630    0.24199   0.894 0.371397    
LOC_34                        -0.18929    0.19364  -0.978 0.328310    
LOC_35                         0.45663    0.14874   3.070 0.002141 ** 
LOC_38                         0.47278    0.36514   1.295 0.195389    
LOC_39                        -0.30191    0.14049  -2.149 0.031641 *  
LOC_40                         0.16437    0.16347   1.006 0.314643    
LOC_42                        -0.30792    0.14153  -2.176 0.029583 *  
LOC_44                         0.32726    0.49046   0.667 0.504620    
LOC_46                         0.79902    0.25188   3.172 0.001513 ** 
LOC_47                        -0.19524    0.20784  -0.939 0.347523    
LOC_48                         0.23997    0.09010   2.663 0.007739 ** 
LOC_49                        -0.19943    0.20672  -0.965 0.334687    
LOC_50                        -0.42946    0.59141  -0.726 0.467737    
LOC_51                        -0.16344    0.07625  -2.144 0.032061 *  
LOC_53                         0.38694    0.12625   3.065 0.002178 ** 
LOC_55                        -0.33457    0.24862  -1.346 0.178397    
TOA_15                         0.06639    0.06903   0.962 0.336158    
TOA_20                         1.12417    0.19269   5.834 5.41e-09 ***
TOA_30                         0.34325    0.08795   3.903 9.50e-05 ***
TOA_35                        -0.54395    0.28615  -1.901 0.057316 .  
TOA_38                         0.33176    0.06677   4.969 6.74e-07 ***
TOA_40                        -0.10655    0.17587  -0.606 0.544633    
TOA_42                         1.14486    0.44113   2.595 0.009451 ** 
TOA_44                         2.15681    1.07182   2.012 0.044190 *  
PPGROUP_11                    -0.40708    0.11663  -3.490 0.000482 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14546  on 14863  degrees of freedom
AIC: 14660

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.496766
LowerLimitAge                 2.250294
BLS_FEDERAL_OtherSep_Rate     1.340601
BLS_FEDERAL_Quits_Rate        1.211541
BLS_FEDERAL_JobOpenings_Level 1.089152
LOSSqrt                       1.729923
SEPCount_EFDATE_OCCLog        1.098668
AGELVL_B                      1.164040
AGELVL_C                      1.660037
AGELVL_E                      1.404476
AGELVL_F                      1.279409
AGELVL_G                      1.266206
AGELVL_H                      1.357132
AGELVL_I                      1.512955
LOC_01                        1.043600
LOC_02                        1.025423
LOC_04                        1.089832
LOC_06                        1.177227
LOC_08                        1.081124
LOC_13                        1.097555
LOC_17                        1.056339
LOC_18                        1.023781
LOC_19                        1.014235
LOC_20                        1.021674
LOC_22                        1.030213
LOC_24                        1.221957
LOC_25                        1.034159
LOC_27                        1.025297
LOC_28                        1.021065
LOC_29                        1.046999
LOC_30                        1.030954
LOC_32                        1.025496
LOC_34                        1.034975
LOC_35                        1.071452
LOC_38                        1.013372
LOC_39                        1.061973
LOC_40                        1.048400
LOC_42                        1.061125
LOC_44                        1.006775
LOC_46                        1.029913
LOC_47                        1.029457
LOC_48                        1.173022
LOC_49                        1.029563
LOC_50                        1.004802
LOC_51                        1.233545
LOC_53                        1.082250
LOC_55                        1.025898
TOA_15                        1.402889
TOA_20                        1.027183
TOA_30                        1.212698
TOA_35                        1.073886
TOA_38                        1.158843
TOA_40                        1.056003
TOA_42                        1.009778
TOA_44                        1.003070
PPGROUP_11                    1.147716

Removed AFTER this step: LOC_28 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8215  -0.7405  -0.1380   0.8185   3.2322  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89401    0.19601  14.765  < 2e-16 ***
GSEGRD                        -0.51956    0.11545  -4.500 6.79e-06 ***
LowerLimitAge                 -2.01084    0.11777 -17.075  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98829    0.08786  11.249  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13872    0.09367   1.481 0.138617    
BLS_FEDERAL_JobOpenings_Level  0.61937    0.06650   9.313  < 2e-16 ***
LOSSqrt                       -6.59396    0.18683 -35.293  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20913    0.10519  -1.988 0.046810 *  
AGELVL_B                      -0.58088    0.21953  -2.646 0.008145 ** 
AGELVL_C                      -0.16653    0.08526  -1.953 0.050798 .  
AGELVL_E                       0.27600    0.06724   4.105 4.05e-05 ***
AGELVL_F                       0.49208    0.07000   7.030 2.06e-12 ***
AGELVL_G                       0.71538    0.07386   9.686  < 2e-16 ***
AGELVL_H                       0.84794    0.07613  11.138  < 2e-16 ***
AGELVL_I                       0.71843    0.08101   8.868  < 2e-16 ***
LOC_01                        -0.22145    0.16579  -1.336 0.181642    
LOC_02                         0.14990    0.24949   0.601 0.547974    
LOC_04                         0.51440    0.13462   3.821 0.000133 ***
LOC_06                         0.40923    0.08494   4.818 1.45e-06 ***
LOC_08                         0.13554    0.12353   1.097 0.272567    
LOC_13                         0.15895    0.11324   1.404 0.160427    
LOC_17                        -0.22865    0.14253  -1.604 0.108684    
LOC_18                        -0.23815    0.24919  -0.956 0.339231    
LOC_19                         0.20386    0.31806   0.641 0.521556    
LOC_20                         0.60265    0.25397   2.373 0.017647 *  
LOC_22                        -0.12158    0.20566  -0.591 0.554412    
LOC_24                        -0.11491    0.08063  -1.425 0.154128    
LOC_25                         0.13914    0.17971   0.774 0.438786    
LOC_27                         0.27938    0.23572   1.185 0.235922    
LOC_29                        -0.36046    0.16687  -2.160 0.030764 *  
LOC_30                         0.67085    0.23798   2.819 0.004818 ** 
LOC_32                         0.22014    0.24189   0.910 0.362780    
LOC_34                        -0.18579    0.19354  -0.960 0.337080    
LOC_35                         0.46038    0.14860   3.098 0.001947 ** 
LOC_38                         0.47674    0.36509   1.306 0.191617    
LOC_39                        -0.29829    0.14035  -2.125 0.033563 *  
LOC_40                         0.16804    0.16335   1.029 0.303616    
LOC_42                        -0.30449    0.14141  -2.153 0.031298 *  
LOC_44                         0.33105    0.49046   0.675 0.499688    
LOC_46                         0.80299    0.25179   3.189 0.001427 ** 
LOC_47                        -0.19150    0.20773  -0.922 0.356608    
LOC_48                         0.24362    0.08988   2.711 0.006716 ** 
LOC_49                        -0.19570    0.20662  -0.947 0.343567    
LOC_50                        -0.42574    0.59139  -0.720 0.471583    
LOC_51                        -0.15995    0.07600  -2.105 0.035313 *  
LOC_53                         0.39062    0.12609   3.098 0.001948 ** 
LOC_55                        -0.33076    0.24853  -1.331 0.183243    
TOA_15                         0.06626    0.06903   0.960 0.337180    
TOA_20                         1.12523    0.19268   5.840 5.23e-09 ***
TOA_30                         0.34421    0.08794   3.914 9.07e-05 ***
TOA_35                        -0.54379    0.28614  -1.900 0.057378 .  
TOA_38                         0.33152    0.06677   4.965 6.86e-07 ***
TOA_40                        -0.10558    0.17586  -0.600 0.548270    
TOA_42                         1.14626    0.44111   2.599 0.009362 ** 
TOA_44                         2.15952    1.07181   2.015 0.043923 *  
PPGROUP_11                    -0.40685    0.11663  -3.488 0.000486 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14546  on 14864  degrees of freedom
AIC: 14658

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.494861
LowerLimitAge                 2.250410
BLS_FEDERAL_OtherSep_Rate     1.340539
BLS_FEDERAL_Quits_Rate        1.211511
BLS_FEDERAL_JobOpenings_Level 1.089136
LOSSqrt                       1.729950
SEPCount_EFDATE_OCCLog        1.098513
AGELVL_B                      1.163762
AGELVL_C                      1.659602
AGELVL_E                      1.404343
AGELVL_F                      1.279406
AGELVL_G                      1.266026
AGELVL_H                      1.357184
AGELVL_I                      1.512943
LOC_01                        1.042083
LOC_02                        1.024650
LOC_04                        1.087148
LOC_06                        1.170599
LOC_08                        1.078309
LOC_13                        1.094198
LOC_17                        1.054332
LOC_18                        1.023077
LOC_19                        1.013767
LOC_20                        1.020968
LOC_22                        1.029161
LOC_24                        1.215632
LOC_25                        1.032952
LOC_27                        1.024447
LOC_29                        1.045386
LOC_30                        1.030052
LOC_32                        1.024698
LOC_34                        1.033927
LOC_35                        1.069356
LOC_38                        1.013003
LOC_39                        1.059792
LOC_40                        1.046769
LOC_42                        1.059184
LOC_44                        1.006589
LOC_46                        1.029125
LOC_47                        1.028426
LOC_48                        1.167075
LOC_49                        1.028528
LOC_50                        1.004679
LOC_51                        1.225560
LOC_53                        1.079402
LOC_55                        1.025149
TOA_15                        1.402840
TOA_20                        1.027086
TOA_30                        1.212261
TOA_35                        1.073691
TOA_38                        1.158770
TOA_40                        1.055898
TOA_42                        1.009745
TOA_44                        1.003050
PPGROUP_11                    1.147690

Removed AFTER this step: LOC_22 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8203  -0.7409  -0.1380   0.8186   3.2332  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.88767    0.19572  14.754  < 2e-16 ***
GSEGRD                        -0.51650    0.11533  -4.478 7.52e-06 ***
LowerLimitAge                 -2.01211    0.11775 -17.088  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98847    0.08786  11.251  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13818    0.09366   1.475 0.140136    
BLS_FEDERAL_JobOpenings_Level  0.61916    0.06650   9.310  < 2e-16 ***
LOSSqrt                       -6.59381    0.18683 -35.292  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20819    0.10519  -1.979 0.047788 *  
AGELVL_B                      -0.57909    0.21950  -2.638 0.008336 ** 
AGELVL_C                      -0.16692    0.08526  -1.958 0.050251 .  
AGELVL_E                       0.27579    0.06724   4.102 4.10e-05 ***
AGELVL_F                       0.49203    0.07000   7.029 2.08e-12 ***
AGELVL_G                       0.71567    0.07385   9.690  < 2e-16 ***
AGELVL_H                       0.84828    0.07613  11.143  < 2e-16 ***
AGELVL_I                       0.71852    0.08101   8.869  < 2e-16 ***
LOC_01                        -0.21724    0.16563  -1.312 0.189670    
LOC_02                         0.15455    0.24935   0.620 0.535390    
LOC_04                         0.51898    0.13440   3.862 0.000113 ***
LOC_06                         0.41359    0.08462   4.888 1.02e-06 ***
LOC_08                         0.13988    0.12331   1.134 0.256652    
LOC_13                         0.16321    0.11301   1.444 0.148672    
LOC_17                        -0.22445    0.14235  -1.577 0.114863    
LOC_18                        -0.23365    0.24907  -0.938 0.348188    
LOC_19                         0.20844    0.31798   0.656 0.512142    
LOC_20                         0.60717    0.25385   2.392 0.016762 *  
LOC_24                        -0.11093    0.08034  -1.381 0.167397    
LOC_25                         0.14328    0.17957   0.798 0.424928    
LOC_27                         0.28394    0.23558   1.205 0.228099    
LOC_29                        -0.35595    0.16669  -2.135 0.032724 *  
LOC_30                         0.67564    0.23784   2.841 0.004500 ** 
LOC_32                         0.22475    0.24177   0.930 0.352573    
LOC_34                        -0.18157    0.19341  -0.939 0.347844    
LOC_35                         0.46490    0.14840   3.133 0.001731 ** 
LOC_38                         0.48160    0.36499   1.319 0.187008    
LOC_39                        -0.29402    0.14016  -2.098 0.035932 *  
LOC_40                         0.17246    0.16318   1.057 0.290560    
LOC_42                        -0.30034    0.14123  -2.127 0.033453 *  
LOC_44                         0.33554    0.49044   0.684 0.493866    
LOC_46                         0.80780    0.25166   3.210 0.001328 ** 
LOC_47                        -0.18705    0.20759  -0.901 0.367566    
LOC_48                         0.24799    0.08957   2.769 0.005627 ** 
LOC_49                        -0.19120    0.20647  -0.926 0.354433    
LOC_50                        -0.42139    0.59135  -0.713 0.476104    
LOC_51                        -0.15577    0.07566  -2.059 0.039513 *  
LOC_53                         0.39508    0.12586   3.139 0.001695 ** 
LOC_55                        -0.32620    0.24841  -1.313 0.189136    
TOA_15                         0.06624    0.06904   0.960 0.337299    
TOA_20                         1.12363    0.19266   5.832 5.47e-09 ***
TOA_30                         0.34540    0.08791   3.929 8.53e-05 ***
TOA_35                        -0.54199    0.28614  -1.894 0.058209 .  
TOA_38                         0.33156    0.06677   4.966 6.84e-07 ***
TOA_40                        -0.10544    0.17586  -0.600 0.548791    
TOA_42                         1.14578    0.44111   2.597 0.009392 ** 
TOA_44                         2.16263    1.07180   2.018 0.043618 *  
PPGROUP_11                    -0.40650    0.11664  -3.485 0.000492 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14546  on 14865  degrees of freedom
AIC: 14656

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.491860
LowerLimitAge                 2.249857
BLS_FEDERAL_OtherSep_Rate     1.340624
BLS_FEDERAL_Quits_Rate        1.211398
BLS_FEDERAL_JobOpenings_Level 1.089165
LOSSqrt                       1.730046
SEPCount_EFDATE_OCCLog        1.098293
AGELVL_B                      1.163518
AGELVL_C                      1.659501
AGELVL_E                      1.404328
AGELVL_F                      1.279390
AGELVL_G                      1.266026
AGELVL_H                      1.357187
AGELVL_I                      1.513125
LOC_01                        1.040148
LOC_02                        1.023631
LOC_04                        1.083542
LOC_06                        1.161719
LOC_08                        1.074488
LOC_13                        1.089741
LOC_17                        1.051699
LOC_18                        1.022117
LOC_19                        1.013164
LOC_20                        1.020041
LOC_24                        1.207112
LOC_25                        1.031373
LOC_27                        1.023343
LOC_29                        1.043193
LOC_30                        1.028860
LOC_32                        1.023631
LOC_34                        1.032500
LOC_35                        1.066512
LOC_38                        1.012487
LOC_39                        1.056964
LOC_40                        1.044558
LOC_42                        1.056548
LOC_44                        1.006347
LOC_46                        1.028047
LOC_47                        1.027067
LOC_48                        1.159125
LOC_49                        1.027123
LOC_50                        1.004522
LOC_51                        1.214843
LOC_53                        1.075524
LOC_55                        1.024151
TOA_15                        1.402810
TOA_20                        1.026875
TOA_30                        1.211641
TOA_35                        1.073533
TOA_38                        1.158785
TOA_40                        1.055889
TOA_42                        1.009746
TOA_44                        1.003025
PPGROUP_11                    1.147630

Removed AFTER this step: TOA_40 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8205  -0.7409  -0.1383   0.8194   3.1997  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.88207    0.19549  14.742  < 2e-16 ***
GSEGRD                        -0.52036    0.11515  -4.519 6.22e-06 ***
LowerLimitAge                 -2.01227    0.11775 -17.089  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98865    0.08785  11.254  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13679    0.09364   1.461 0.144072    
BLS_FEDERAL_JobOpenings_Level  0.61982    0.06649   9.322  < 2e-16 ***
LOSSqrt                       -6.57695    0.18463 -35.623  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21079    0.10511  -2.005 0.044916 *  
AGELVL_B                      -0.57929    0.21952  -2.639 0.008319 ** 
AGELVL_C                      -0.16794    0.08527  -1.970 0.048882 *  
AGELVL_E                       0.27681    0.06722   4.118 3.82e-05 ***
AGELVL_F                       0.49290    0.06998   7.043 1.88e-12 ***
AGELVL_G                       0.71632    0.07384   9.702  < 2e-16 ***
AGELVL_H                       0.84881    0.07611  11.152  < 2e-16 ***
AGELVL_I                       0.71865    0.08099   8.874  < 2e-16 ***
LOC_01                        -0.21545    0.16556  -1.301 0.193144    
LOC_02                         0.15555    0.24930   0.624 0.532662    
LOC_04                         0.52041    0.13436   3.873 0.000107 ***
LOC_06                         0.41432    0.08460   4.898 9.70e-07 ***
LOC_08                         0.14097    0.12329   1.143 0.252877    
LOC_13                         0.16490    0.11296   1.460 0.144341    
LOC_17                        -0.22306    0.14233  -1.567 0.117053    
LOC_18                        -0.23485    0.24908  -0.943 0.345744    
LOC_19                         0.20971    0.31789   0.660 0.509439    
LOC_20                         0.60797    0.25379   2.396 0.016594 *  
LOC_24                        -0.11263    0.08033  -1.402 0.160916    
LOC_25                         0.14415    0.17952   0.803 0.421995    
LOC_27                         0.28511    0.23553   1.211 0.226079    
LOC_29                        -0.35474    0.16662  -2.129 0.033248 *  
LOC_30                         0.67553    0.23783   2.840 0.004505 ** 
LOC_32                         0.22604    0.24170   0.935 0.349680    
LOC_34                        -0.17997    0.19337  -0.931 0.352021    
LOC_35                         0.46511    0.14838   3.135 0.001721 ** 
LOC_38                         0.48280    0.36489   1.323 0.185786    
LOC_39                        -0.29273    0.14012  -2.089 0.036699 *  
LOC_40                         0.17440    0.16310   1.069 0.284950    
LOC_42                        -0.30097    0.14126  -2.131 0.033115 *  
LOC_44                         0.33044    0.49130   0.673 0.501214    
LOC_46                         0.80935    0.25159   3.217 0.001296 ** 
LOC_47                        -0.18636    0.20753  -0.898 0.369194    
LOC_48                         0.24883    0.08954   2.779 0.005455 ** 
LOC_49                        -0.18952    0.20641  -0.918 0.358534    
LOC_50                        -0.41935    0.59124  -0.709 0.478158    
LOC_51                        -0.15505    0.07565  -2.050 0.040407 *  
LOC_53                         0.39664    0.12581   3.153 0.001618 ** 
LOC_55                        -0.32458    0.24833  -1.307 0.191198    
TOA_15                         0.07140    0.06849   1.043 0.297144    
TOA_20                         1.12720    0.19252   5.855 4.77e-09 ***
TOA_30                         0.34954    0.08761   3.990 6.62e-05 ***
TOA_35                        -0.53699    0.28600  -1.878 0.060434 .  
TOA_38                         0.33487    0.06652   5.034 4.80e-07 ***
TOA_42                         1.15044    0.44105   2.608 0.009097 ** 
TOA_44                         2.16669    1.07171   2.022 0.043207 *  
PPGROUP_11                    -0.40548    0.11663  -3.477 0.000508 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14547  on 14866  degrees of freedom
AIC: 14655

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.487185
LowerLimitAge                 2.249881
BLS_FEDERAL_OtherSep_Rate     1.340745
BLS_FEDERAL_Quits_Rate        1.210656
BLS_FEDERAL_JobOpenings_Level 1.088865
LOSSqrt                       1.689806
SEPCount_EFDATE_OCCLog        1.096528
AGELVL_B                      1.163546
AGELVL_C                      1.658576
AGELVL_E                      1.403573
AGELVL_F                      1.278909
AGELVL_G                      1.265762
AGELVL_H                      1.356983
AGELVL_I                      1.513202
LOC_01                        1.039832
LOC_02                        1.023585
LOC_04                        1.083224
LOC_06                        1.161538
LOC_08                        1.074263
LOC_13                        1.089078
LOC_17                        1.051427
LOC_18                        1.022055
LOC_19                        1.013121
LOC_20                        1.020015
LOC_24                        1.205349
LOC_25                        1.031318
LOC_27                        1.023271
LOC_29                        1.043059
LOC_30                        1.028833
LOC_32                        1.023553
LOC_34                        1.032307
LOC_35                        1.066566
LOC_38                        1.012456
LOC_39                        1.056736
LOC_40                        1.044162
LOC_42                        1.056481
LOC_44                        1.006012
LOC_46                        1.027944
LOC_47                        1.027047
LOC_48                        1.158926
LOC_49                        1.026938
LOC_50                        1.004489
LOC_51                        1.214545
LOC_53                        1.075086
LOC_55                        1.024033
TOA_15                        1.380826
TOA_20                        1.025897
TOA_30                        1.204261
TOA_35                        1.072641
TOA_38                        1.150835
TOA_42                        1.009426
TOA_44                        1.002985
PPGROUP_11                    1.147404

Removed AFTER this step: LOC_02 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8213  -0.7410  -0.1383   0.8191   3.1986  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.88812    0.19526  14.792  < 2e-16 ***
GSEGRD                        -0.52476    0.11494  -4.566 4.98e-06 ***
LowerLimitAge                 -2.01189    0.11775 -17.087  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98890    0.08785  11.257  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13682    0.09364   1.461  0.14399    
BLS_FEDERAL_JobOpenings_Level  0.61968    0.06649   9.320  < 2e-16 ***
LOSSqrt                       -6.57452    0.18456 -35.623  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21101    0.10511  -2.008  0.04469 *  
AGELVL_B                      -0.58143    0.21950  -2.649  0.00808 ** 
AGELVL_C                      -0.16870    0.08526  -1.979  0.04785 *  
AGELVL_E                       0.27637    0.06721   4.112 3.93e-05 ***
AGELVL_F                       0.49348    0.06998   7.052 1.76e-12 ***
AGELVL_G                       0.71662    0.07383   9.706  < 2e-16 ***
AGELVL_H                       0.84968    0.07610  11.166  < 2e-16 ***
AGELVL_I                       0.71872    0.08099   8.875  < 2e-16 ***
LOC_01                        -0.21909    0.16545  -1.324  0.18544    
LOC_04                         0.51623    0.13419   3.847  0.00012 ***
LOC_06                         0.41057    0.08438   4.866 1.14e-06 ***
LOC_08                         0.13703    0.12313   1.113  0.26577    
LOC_13                         0.16113    0.11280   1.429  0.15315    
LOC_17                        -0.22666    0.14220  -1.594  0.11094    
LOC_18                        -0.23835    0.24900  -0.957  0.33846    
LOC_19                         0.20595    0.31778   0.648  0.51693    
LOC_20                         0.60378    0.25369   2.380  0.01731 *  
LOC_24                        -0.11595    0.08016  -1.447  0.14802    
LOC_25                         0.14057    0.17942   0.783  0.43335    
LOC_27                         0.28108    0.23543   1.194  0.23251    
LOC_29                        -0.35862    0.16650  -2.154  0.03125 *  
LOC_30                         0.67102    0.23771   2.823  0.00476 ** 
LOC_32                         0.22222    0.24161   0.920  0.35771    
LOC_34                        -0.18341    0.19329  -0.949  0.34266    
LOC_35                         0.46097    0.14822   3.110  0.00187 ** 
LOC_38                         0.47844    0.36480   1.312  0.18968    
LOC_39                        -0.29633    0.14000  -2.117  0.03428 *  
LOC_40                         0.17045    0.16297   1.046  0.29562    
LOC_42                        -0.30437    0.14114  -2.156  0.03105 *  
LOC_44                         0.32640    0.49119   0.665  0.50636    
LOC_46                         0.80482    0.25148   3.200  0.00137 ** 
LOC_47                        -0.19012    0.20743  -0.917  0.35938    
LOC_48                         0.24494    0.08932   2.742  0.00610 ** 
LOC_49                        -0.19341    0.20631  -0.937  0.34852    
LOC_50                        -0.42294    0.59118  -0.715  0.47434    
LOC_51                        -0.15869    0.07543  -2.104  0.03540 *  
LOC_53                         0.39271    0.12565   3.126  0.00177 ** 
LOC_55                        -0.32816    0.24825  -1.322  0.18619    
TOA_15                         0.07185    0.06848   1.049  0.29410    
TOA_20                         1.12800    0.19248   5.860 4.62e-09 ***
TOA_30                         0.34858    0.08760   3.979 6.91e-05 ***
TOA_35                        -0.53922    0.28599  -1.885  0.05937 .  
TOA_38                         0.33377    0.06649   5.020 5.18e-07 ***
TOA_42                         1.14915    0.44106   2.605  0.00918 ** 
TOA_44                         2.16420    1.07170   2.019  0.04344 *  
PPGROUP_11                    -0.40595    0.11663  -3.481  0.00050 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14547  on 14867  degrees of freedom
AIC: 14653

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.481654
LowerLimitAge                 2.249661
BLS_FEDERAL_OtherSep_Rate     1.340748
BLS_FEDERAL_Quits_Rate        1.210664
BLS_FEDERAL_JobOpenings_Level 1.088837
LOSSqrt                       1.688752
SEPCount_EFDATE_OCCLog        1.096531
AGELVL_B                      1.163278
AGELVL_C                      1.658219
AGELVL_E                      1.403404
AGELVL_F                      1.278689
AGELVL_G                      1.265681
AGELVL_H                      1.356492
AGELVL_I                      1.513072
LOC_01                        1.038549
LOC_04                        1.080522
LOC_06                        1.155708
LOC_08                        1.071440
LOC_13                        1.085967
LOC_17                        1.049719
LOC_18                        1.021546
LOC_19                        1.012762
LOC_20                        1.019301
LOC_24                        1.200105
LOC_25                        1.030274
LOC_27                        1.022506
LOC_29                        1.041619
LOC_30                        1.027878
LOC_32                        1.022898
LOC_34                        1.031483
LOC_35                        1.064436
LOC_38                        1.012088
LOC_39                        1.054960
LOC_40                        1.042594
LOC_42                        1.054934
LOC_44                        1.005838
LOC_46                        1.027085
LOC_47                        1.026188
LOC_48                        1.153324
LOC_49                        1.026005
LOC_50                        1.004394
LOC_51                        1.207361
LOC_53                        1.072411
LOC_55                        1.023498
TOA_15                        1.380722
TOA_20                        1.025931
TOA_30                        1.203909
TOA_35                        1.072500
TOA_38                        1.150034
TOA_42                        1.009406
TOA_44                        1.002972
PPGROUP_11                    1.147358

Removed AFTER this step: LOC_19 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8214  -0.7410  -0.1383   0.8191   3.1978  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89198    0.19516  14.818  < 2e-16 ***
GSEGRD                        -0.52650    0.11491  -4.582 4.61e-06 ***
LowerLimitAge                 -2.01097    0.11774 -17.080  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98878    0.08785  11.256  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13678    0.09364   1.461 0.144084    
BLS_FEDERAL_JobOpenings_Level  0.61997    0.06649   9.324  < 2e-16 ***
LOSSqrt                       -6.57398    0.18455 -35.622  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21243    0.10509  -2.021 0.043232 *  
AGELVL_B                      -0.58043    0.21952  -2.644 0.008192 ** 
AGELVL_C                      -0.16846    0.08525  -1.976 0.048152 *  
AGELVL_E                       0.27667    0.06722   4.116 3.85e-05 ***
AGELVL_F                       0.49364    0.06998   7.054 1.73e-12 ***
AGELVL_G                       0.71617    0.07383   9.700  < 2e-16 ***
AGELVL_H                       0.84958    0.07610  11.165  < 2e-16 ***
AGELVL_I                       0.71871    0.08098   8.875  < 2e-16 ***
LOC_01                        -0.22203    0.16539  -1.342 0.179461    
LOC_04                         0.51332    0.13411   3.828 0.000129 ***
LOC_06                         0.40760    0.08425   4.838 1.31e-06 ***
LOC_08                         0.13426    0.12306   1.091 0.275263    
LOC_13                         0.15834    0.11271   1.405 0.160075    
LOC_17                        -0.22948    0.14213  -1.615 0.106404    
LOC_18                        -0.24162    0.24895  -0.971 0.331764    
LOC_20                         0.60089    0.25364   2.369 0.017833 *  
LOC_24                        -0.11852    0.08006  -1.480 0.138761    
LOC_25                         0.13774    0.17937   0.768 0.442531    
LOC_27                         0.27786    0.23538   1.180 0.237809    
LOC_29                        -0.36170    0.16643  -2.173 0.029757 *  
LOC_30                         0.66790    0.23765   2.810 0.004947 ** 
LOC_32                         0.21889    0.24156   0.906 0.364851    
LOC_34                        -0.18633    0.19324  -0.964 0.334922    
LOC_35                         0.45815    0.14816   3.092 0.001986 ** 
LOC_38                         0.47528    0.36475   1.303 0.192561    
LOC_39                        -0.29932    0.13992  -2.139 0.032423 *  
LOC_40                         0.16760    0.16291   1.029 0.303573    
LOC_42                        -0.30721    0.14108  -2.178 0.029433 *  
LOC_44                         0.32327    0.49115   0.658 0.510411    
LOC_46                         0.80174    0.25142   3.189 0.001429 ** 
LOC_47                        -0.19322    0.20738  -0.932 0.351463    
LOC_48                         0.24207    0.08921   2.713 0.006659 ** 
LOC_49                        -0.19644    0.20625  -0.952 0.340882    
LOC_50                        -0.42601    0.59117  -0.721 0.471137    
LOC_51                        -0.16138    0.07532  -2.143 0.032147 *  
LOC_53                         0.38977    0.12556   3.104 0.001908 ** 
LOC_55                        -0.33154    0.24819  -1.336 0.181609    
TOA_15                         0.07139    0.06848   1.043 0.297180    
TOA_20                         1.12703    0.19248   5.855 4.76e-09 ***
TOA_30                         0.34755    0.08758   3.968 7.23e-05 ***
TOA_35                        -0.54109    0.28598  -1.892 0.058483 .  
TOA_38                         0.33494    0.06646   5.040 4.66e-07 ***
TOA_42                         1.14784    0.44106   2.602 0.009256 ** 
TOA_44                         2.16192    1.07168   2.017 0.043663 *  
PPGROUP_11                    -0.40566    0.11662  -3.478 0.000504 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14548  on 14868  degrees of freedom
AIC: 14652

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.480809
LowerLimitAge                 2.249496
BLS_FEDERAL_OtherSep_Rate     1.340705
BLS_FEDERAL_Quits_Rate        1.210674
BLS_FEDERAL_JobOpenings_Level 1.088762
LOSSqrt                       1.688682
SEPCount_EFDATE_OCCLog        1.096064
AGELVL_B                      1.163200
AGELVL_C                      1.658214
AGELVL_E                      1.403332
AGELVL_F                      1.278691
AGELVL_G                      1.265575
AGELVL_H                      1.356512
AGELVL_I                      1.513122
LOC_01                        1.037773
LOC_04                        1.079344
LOC_06                        1.152315
LOC_08                        1.070173
LOC_13                        1.084412
LOC_17                        1.048750
LOC_18                        1.021126
LOC_20                        1.018992
LOC_24                        1.197217
LOC_25                        1.029670
LOC_27                        1.022055
LOC_29                        1.040782
LOC_30                        1.027463
LOC_32                        1.022438
LOC_34                        1.030931
LOC_35                        1.063537
LOC_38                        1.011912
LOC_39                        1.053827
LOC_40                        1.041853
LOC_42                        1.053930
LOC_44                        1.005741
LOC_46                        1.026727
LOC_47                        1.025646
LOC_48                        1.150540
LOC_49                        1.025485
LOC_50                        1.004330
LOC_51                        1.203790
LOC_53                        1.071031
LOC_55                        1.023050
TOA_15                        1.380558
TOA_20                        1.025870
TOA_30                        1.203504
TOA_35                        1.072384
TOA_38                        1.149319
TOA_42                        1.009386
TOA_44                        1.002961
PPGROUP_11                    1.147375

Removed AFTER this step: LOC_44 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8213  -0.7408  -0.1384   0.8193   3.1969  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89446    0.19512  14.834  < 2e-16 ***
GSEGRD                        -0.52778    0.11489  -4.594 4.36e-06 ***
LowerLimitAge                 -2.00949    0.11772 -17.071  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98813    0.08784  11.249  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13724    0.09364   1.466 0.142742    
BLS_FEDERAL_JobOpenings_Level  0.61918    0.06648   9.314  < 2e-16 ***
LOSSqrt                       -6.57328    0.18454 -35.620  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21278    0.10508  -2.025 0.042881 *  
AGELVL_B                      -0.57963    0.21949  -2.641 0.008271 ** 
AGELVL_C                      -0.16866    0.08525  -1.978 0.047875 *  
AGELVL_E                       0.27645    0.06721   4.113 3.90e-05 ***
AGELVL_F                       0.49308    0.06997   7.047 1.83e-12 ***
AGELVL_G                       0.71540    0.07382   9.691  < 2e-16 ***
AGELVL_H                       0.84910    0.07609  11.159  < 2e-16 ***
AGELVL_I                       0.71828    0.08098   8.870  < 2e-16 ***
LOC_01                        -0.22388    0.16536  -1.354 0.175777    
LOC_04                         0.51130    0.13406   3.814 0.000137 ***
LOC_06                         0.40571    0.08420   4.818 1.45e-06 ***
LOC_08                         0.13240    0.12302   1.076 0.281797    
LOC_13                         0.15653    0.11267   1.389 0.164770    
LOC_17                        -0.23130    0.14210  -1.628 0.103588    
LOC_18                        -0.24346    0.24891  -0.978 0.328026    
LOC_20                         0.59889    0.25360   2.362 0.018199 *  
LOC_24                        -0.12018    0.08002  -1.502 0.133113    
LOC_25                         0.13598    0.17933   0.758 0.448292    
LOC_27                         0.27599    0.23536   1.173 0.240934    
LOC_29                        -0.36360    0.16639  -2.185 0.028877 *  
LOC_30                         0.66568    0.23761   2.802 0.005085 ** 
LOC_32                         0.21692    0.24152   0.898 0.369109    
LOC_34                        -0.18804    0.19321  -0.973 0.330436    
LOC_35                         0.45615    0.14812   3.080 0.002072 ** 
LOC_38                         0.47322    0.36471   1.298 0.194442    
LOC_39                        -0.30115    0.13989  -2.153 0.031334 *  
LOC_40                         0.16575    0.16287   1.018 0.308835    
LOC_42                        -0.30896    0.14104  -2.191 0.028483 *  
LOC_46                         0.79959    0.25138   3.181 0.001469 ** 
LOC_47                        -0.19518    0.20734  -0.941 0.346539    
LOC_48                         0.24017    0.08916   2.694 0.007067 ** 
LOC_49                        -0.19829    0.20622  -0.962 0.336269    
LOC_50                        -0.42785    0.59110  -0.724 0.469177    
LOC_51                        -0.16312    0.07527  -2.167 0.030230 *  
LOC_53                         0.38788    0.12552   3.090 0.002000 ** 
LOC_55                        -0.33344    0.24816  -1.344 0.179066    
TOA_15                         0.07119    0.06847   1.040 0.298486    
TOA_20                         1.12625    0.19246   5.852 4.86e-09 ***
TOA_30                         0.34718    0.08757   3.965 7.35e-05 ***
TOA_35                        -0.54221    0.28597  -1.896 0.057956 .  
TOA_38                         0.33472    0.06646   5.037 4.74e-07 ***
TOA_42                         1.14707    0.44106   2.601 0.009302 ** 
TOA_44                         2.16066    1.07168   2.016 0.043785 *  
PPGROUP_11                    -0.40547    0.11661  -3.477 0.000507 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14548  on 14869  degrees of freedom
AIC: 14650

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.480452
LowerLimitAge                 2.248635
BLS_FEDERAL_OtherSep_Rate     1.340564
BLS_FEDERAL_Quits_Rate        1.210606
BLS_FEDERAL_JobOpenings_Level 1.088423
LOSSqrt                       1.688586
SEPCount_EFDATE_OCCLog        1.096081
AGELVL_B                      1.163230
AGELVL_C                      1.658207
AGELVL_E                      1.403348
AGELVL_F                      1.278508
AGELVL_G                      1.265252
AGELVL_H                      1.356430
AGELVL_I                      1.513087
LOC_01                        1.037476
LOC_04                        1.078792
LOC_06                        1.151009
LOC_08                        1.069624
LOC_13                        1.083775
LOC_17                        1.048364
LOC_18                        1.021003
LOC_20                        1.018850
LOC_24                        1.196049
LOC_25                        1.029446
LOC_27                        1.021906
LOC_29                        1.040475
LOC_30                        1.027260
LOC_32                        1.022286
LOC_34                        1.030748
LOC_35                        1.063099
LOC_38                        1.011841
LOC_39                        1.053416
LOC_40                        1.041553
LOC_42                        1.053561
LOC_46                        1.026558
LOC_47                        1.025440
LOC_48                        1.149361
LOC_49                        1.025299
LOC_50                        1.004308
LOC_51                        1.202324
LOC_53                        1.070487
LOC_55                        1.022915
TOA_15                        1.380541
TOA_20                        1.025829
TOA_30                        1.203452
TOA_35                        1.072314
TOA_38                        1.149291
TOA_42                        1.009379
TOA_44                        1.002957
PPGROUP_11                    1.147387

Removed AFTER this step: LOC_50 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8216  -0.7414  -0.1384   0.8195   3.1974  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.89405    0.19512  14.832  < 2e-16 ***
GSEGRD                        -0.52809    0.11489  -4.596 4.30e-06 ***
LowerLimitAge                 -2.01068    0.11770 -17.083  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98786    0.08784  11.246  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13713    0.09363   1.465 0.143023    
BLS_FEDERAL_JobOpenings_Level  0.61944    0.06648   9.318  < 2e-16 ***
LOSSqrt                       -6.57167    0.18452 -35.614  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21280    0.10508  -2.025 0.042851 *  
AGELVL_B                      -0.57953    0.21949  -2.640 0.008281 ** 
AGELVL_C                      -0.16888    0.08525  -1.981 0.047578 *  
AGELVL_E                       0.27682    0.06721   4.119 3.81e-05 ***
AGELVL_F                       0.49290    0.06997   7.044 1.86e-12 ***
AGELVL_G                       0.71482    0.07381   9.685  < 2e-16 ***
AGELVL_H                       0.84953    0.07609  11.165  < 2e-16 ***
AGELVL_I                       0.71781    0.08098   8.864  < 2e-16 ***
LOC_01                        -0.22220    0.16534  -1.344 0.179002    
LOC_04                         0.51305    0.13404   3.828 0.000129 ***
LOC_06                         0.40739    0.08417   4.840 1.30e-06 ***
LOC_08                         0.13406    0.12299   1.090 0.275718    
LOC_13                         0.15816    0.11265   1.404 0.160305    
LOC_17                        -0.22970    0.14208  -1.617 0.105939    
LOC_18                        -0.24157    0.24889  -0.971 0.331744    
LOC_20                         0.60068    0.25358   2.369 0.017847 *  
LOC_24                        -0.11857    0.07998  -1.482 0.138225    
LOC_25                         0.13758    0.17932   0.767 0.442949    
LOC_27                         0.27768    0.23534   1.180 0.238026    
LOC_29                        -0.36193    0.16637  -2.175 0.029601 *  
LOC_30                         0.66741    0.23760   2.809 0.004970 ** 
LOC_32                         0.21886    0.24150   0.906 0.364805    
LOC_34                        -0.18643    0.19320  -0.965 0.334565    
LOC_35                         0.45781    0.14810   3.091 0.001993 ** 
LOC_38                         0.47499    0.36469   1.302 0.192770    
LOC_39                        -0.29945    0.13987  -2.141 0.032276 *  
LOC_40                         0.16736    0.16286   1.028 0.304100    
LOC_42                        -0.30733    0.14102  -2.179 0.029309 *  
LOC_46                         0.80126    0.25137   3.188 0.001435 ** 
LOC_47                        -0.19338    0.20732  -0.933 0.350954    
LOC_48                         0.24190    0.08913   2.714 0.006646 ** 
LOC_49                        -0.19656    0.20620  -0.953 0.340469    
LOC_51                        -0.16152    0.07524  -2.147 0.031805 *  
LOC_53                         0.38951    0.12550   3.104 0.001912 ** 
LOC_55                        -0.33164    0.24814  -1.337 0.181382    
TOA_15                         0.07098    0.06847   1.037 0.299876    
TOA_20                         1.12672    0.19246   5.854 4.79e-09 ***
TOA_30                         0.34808    0.08756   3.975 7.03e-05 ***
TOA_35                        -0.54184    0.28597  -1.895 0.058122 .  
TOA_38                         0.33395    0.06645   5.026 5.01e-07 ***
TOA_42                         1.14770    0.44106   2.602 0.009264 ** 
TOA_44                         2.16218    1.07167   2.018 0.043636 *  
PPGROUP_11                    -0.40631    0.11660  -3.485 0.000493 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14549  on 14870  degrees of freedom
AIC: 14649

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.480438
LowerLimitAge                 2.248104
BLS_FEDERAL_OtherSep_Rate     1.340503
BLS_FEDERAL_Quits_Rate        1.210604
BLS_FEDERAL_JobOpenings_Level 1.088412
LOSSqrt                       1.688238
SEPCount_EFDATE_OCCLog        1.096052
AGELVL_B                      1.163210
AGELVL_C                      1.658174
AGELVL_E                      1.403241
AGELVL_F                      1.278470
AGELVL_G                      1.265036
AGELVL_H                      1.356328
AGELVL_I                      1.512796
LOC_01                        1.037272
LOC_04                        1.078435
LOC_06                        1.150122
LOC_08                        1.069244
LOC_13                        1.083333
LOC_17                        1.048109
LOC_18                        1.020890
LOC_20                        1.018751
LOC_24                        1.195107
LOC_25                        1.029288
LOC_27                        1.021805
LOC_29                        1.040272
LOC_30                        1.027153
LOC_32                        1.022159
LOC_34                        1.030606
LOC_35                        1.062835
LOC_38                        1.011793
LOC_39                        1.053115
LOC_40                        1.041352
LOC_42                        1.053284
LOC_46                        1.026469
LOC_47                        1.025291
LOC_48                        1.148528
LOC_49                        1.025159
LOC_51                        1.201261
LOC_53                        1.070134
LOC_55                        1.022810
TOA_15                        1.380607
TOA_20                        1.025817
TOA_30                        1.203214
TOA_35                        1.072308
TOA_38                        1.148895
TOA_42                        1.009373
TOA_44                        1.002954
PPGROUP_11                    1.147269

Removed AFTER this step: LOC_25 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8225  -0.7409  -0.1384   0.8199   3.1958  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.90010    0.19497  14.875  < 2e-16 ***
GSEGRD                        -0.52972    0.11487  -4.612 4.00e-06 ***
LowerLimitAge                 -2.01119    0.11770 -17.087  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98892    0.08783  11.259  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13642    0.09363   1.457 0.145089    
BLS_FEDERAL_JobOpenings_Level  0.61970    0.06647   9.323  < 2e-16 ***
LOSSqrt                       -6.57076    0.18452 -35.611  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21279    0.10508  -2.025 0.042868 *  
AGELVL_B                      -0.57709    0.21942  -2.630 0.008536 ** 
AGELVL_C                      -0.16880    0.08524  -1.980 0.047677 *  
AGELVL_E                       0.27677    0.06720   4.118 3.82e-05 ***
AGELVL_F                       0.49325    0.06996   7.050 1.79e-12 ***
AGELVL_G                       0.71465    0.07381   9.683  < 2e-16 ***
AGELVL_H                       0.84974    0.07609  11.168  < 2e-16 ***
AGELVL_I                       0.71824    0.08098   8.869  < 2e-16 ***
LOC_01                        -0.22788    0.16518  -1.380 0.167710    
LOC_04                         0.50741    0.13384   3.791 0.000150 ***
LOC_06                         0.40173    0.08385   4.791 1.66e-06 ***
LOC_08                         0.12845    0.12278   1.046 0.295471    
LOC_13                         0.15256    0.11242   1.357 0.174744    
LOC_17                        -0.23535    0.14189  -1.659 0.097189 .  
LOC_18                        -0.24727    0.24878  -0.994 0.320263    
LOC_20                         0.59506    0.25347   2.348 0.018893 *  
LOC_24                        -0.12396    0.07968  -1.556 0.119764    
LOC_27                         0.27182    0.23522   1.156 0.247850    
LOC_29                        -0.36768    0.16621  -2.212 0.026958 *  
LOC_30                         0.66165    0.23748   2.786 0.005334 ** 
LOC_32                         0.21314    0.24139   0.883 0.377252    
LOC_34                        -0.19215    0.19306  -0.995 0.319595    
LOC_35                         0.45214    0.14791   3.057 0.002237 ** 
LOC_38                         0.46932    0.36461   1.287 0.198039    
LOC_39                        -0.30510    0.13968  -2.184 0.028938 *  
LOC_40                         0.16169    0.16269   0.994 0.320297    
LOC_42                        -0.31301    0.14083  -2.223 0.026244 *  
LOC_46                         0.79548    0.25125   3.166 0.001545 ** 
LOC_47                        -0.19906    0.20720  -0.961 0.336685    
LOC_48                         0.23631    0.08883   2.660 0.007809 ** 
LOC_49                        -0.20228    0.20607  -0.982 0.326300    
LOC_51                        -0.16708    0.07489  -2.231 0.025684 *  
LOC_53                         0.38378    0.12528   3.063 0.002189 ** 
LOC_55                        -0.33740    0.24804  -1.360 0.173742    
TOA_15                         0.07061    0.06846   1.031 0.302377    
TOA_20                         1.12870    0.19243   5.865 4.48e-09 ***
TOA_30                         0.34817    0.08756   3.976 7.00e-05 ***
TOA_35                        -0.54411    0.28594  -1.903 0.057054 .  
TOA_38                         0.33408    0.06644   5.028 4.94e-07 ***
TOA_42                         1.14552    0.44106   2.597 0.009399 ** 
TOA_44                         2.15706    1.07165   2.013 0.044131 *  
PPGROUP_11                    -0.40608    0.11661  -3.482 0.000497 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14549  on 14871  degrees of freedom
AIC: 14647

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.479926
LowerLimitAge                 2.248229
BLS_FEDERAL_OtherSep_Rate     1.340278
BLS_FEDERAL_Quits_Rate        1.210517
BLS_FEDERAL_JobOpenings_Level 1.088348
LOSSqrt                       1.688115
SEPCount_EFDATE_OCCLog        1.096060
AGELVL_B                      1.162989
AGELVL_C                      1.658226
AGELVL_E                      1.403223
AGELVL_F                      1.278456
AGELVL_G                      1.265052
AGELVL_H                      1.356344
AGELVL_I                      1.512637
LOC_01                        1.035198
LOC_04                        1.075230
LOC_06                        1.141334
LOC_08                        1.065492
LOC_13                        1.078816
LOC_17                        1.045329
LOC_18                        1.019989
LOC_20                        1.017908
LOC_24                        1.185992
LOC_27                        1.020732
LOC_29                        1.038175
LOC_30                        1.026131
LOC_32                        1.021193
LOC_34                        1.029083
LOC_35                        1.060207
LOC_38                        1.011383
LOC_39                        1.050218
LOC_40                        1.039225
LOC_42                        1.050413
LOC_46                        1.025551
LOC_47                        1.023994
LOC_48                        1.140914
LOC_49                        1.023830
LOC_51                        1.190253
LOC_53                        1.066363
LOC_55                        1.021883
TOA_15                        1.380547
TOA_20                        1.025636
TOA_30                        1.203224
TOA_35                        1.072257
TOA_38                        1.148892
TOA_42                        1.009336
TOA_44                        1.002915
PPGROUP_11                    1.147275

Removed AFTER this step: LOC_32 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8245  -0.7411  -0.1384   0.8200   3.1946  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.90727    0.19479  14.925  < 2e-16 ***
GSEGRD                        -0.53247    0.11482  -4.637 3.53e-06 ***
LowerLimitAge                 -2.00908    0.11767 -17.074  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98833    0.08783  11.253  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13652    0.09362   1.458 0.144794    
BLS_FEDERAL_JobOpenings_Level  0.61944    0.06647   9.319  < 2e-16 ***
LOSSqrt                       -6.57280    0.18449 -35.627  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21539    0.10504  -2.051 0.040311 *  
AGELVL_B                      -0.57941    0.21942  -2.641 0.008274 ** 
AGELVL_C                      -0.16957    0.08523  -1.989 0.046649 *  
AGELVL_E                       0.27597    0.06720   4.107 4.01e-05 ***
AGELVL_F                       0.49354    0.06995   7.055 1.72e-12 ***
AGELVL_G                       0.71406    0.07381   9.674  < 2e-16 ***
AGELVL_H                       0.84935    0.07608  11.164  < 2e-16 ***
AGELVL_I                       0.71785    0.08097   8.865  < 2e-16 ***
LOC_01                        -0.23262    0.16511  -1.409 0.158855    
LOC_04                         0.50242    0.13372   3.757 0.000172 ***
LOC_06                         0.39672    0.08366   4.742 2.11e-06 ***
LOC_08                         0.12370    0.12267   1.008 0.313248    
LOC_13                         0.14794    0.11230   1.317 0.187701    
LOC_17                        -0.24002    0.14180  -1.693 0.090519 .  
LOC_18                        -0.25285    0.24872  -1.017 0.309342    
LOC_20                         0.59011    0.25342   2.329 0.019882 *  
LOC_24                        -0.12822    0.07954  -1.612 0.106955    
LOC_27                         0.26644    0.23517   1.133 0.257219    
LOC_29                        -0.37286    0.16613  -2.244 0.024802 *  
LOC_30                         0.65640    0.23741   2.765 0.005696 ** 
LOC_34                        -0.19681    0.19299  -1.020 0.307844    
LOC_35                         0.44742    0.14782   3.027 0.002471 ** 
LOC_38                         0.46404    0.36457   1.273 0.203064    
LOC_39                        -0.31006    0.13957  -2.221 0.026319 *  
LOC_40                         0.15702    0.16260   0.966 0.334222    
LOC_42                        -0.31763    0.14074  -2.257 0.024020 *  
LOC_46                         0.79037    0.25119   3.146 0.001652 ** 
LOC_47                        -0.20441    0.20713  -0.987 0.323702    
LOC_48                         0.23148    0.08867   2.611 0.009036 ** 
LOC_49                        -0.20735    0.20601  -1.007 0.314166    
LOC_51                        -0.17154    0.07473  -2.295 0.021709 *  
LOC_53                         0.37902    0.12516   3.028 0.002460 ** 
LOC_55                        -0.34326    0.24797  -1.384 0.166273    
TOA_15                         0.07048    0.06846   1.029 0.303253    
TOA_20                         1.13168    0.19251   5.878 4.14e-09 ***
TOA_30                         0.34725    0.08755   3.966 7.30e-05 ***
TOA_35                        -0.54646    0.28595  -1.911 0.055998 .  
TOA_38                         0.33662    0.06637   5.072 3.94e-07 ***
TOA_42                         1.14322    0.44106   2.592 0.009543 ** 
TOA_44                         2.15337    1.07164   2.009 0.044493 *  
PPGROUP_11                    -0.40500    0.11660  -3.473 0.000514 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14550  on 14872  degrees of freedom
AIC: 14646

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.478702
LowerLimitAge                 2.247078
BLS_FEDERAL_OtherSep_Rate     1.340209
BLS_FEDERAL_Quits_Rate        1.210590
BLS_FEDERAL_JobOpenings_Level 1.088248
LOSSqrt                       1.687936
SEPCount_EFDATE_OCCLog        1.095150
AGELVL_B                      1.162859
AGELVL_C                      1.657980
AGELVL_E                      1.402918
AGELVL_F                      1.278470
AGELVL_G                      1.264804
AGELVL_H                      1.356124
AGELVL_I                      1.512502
LOC_01                        1.034110
LOC_04                        1.073342
LOC_06                        1.136166
LOC_08                        1.063477
LOC_13                        1.076514
LOC_17                        1.043897
LOC_18                        1.019338
LOC_20                        1.017415
LOC_24                        1.181750
LOC_27                        1.020054
LOC_29                        1.036893
LOC_30                        1.025489
LOC_34                        1.028337
LOC_35                        1.058841
LOC_38                        1.011119
LOC_39                        1.048542
LOC_40                        1.038150
LOC_42                        1.048995
LOC_46                        1.025012
LOC_47                        1.023128
LOC_48                        1.136639
LOC_49                        1.023045
LOC_51                        1.184976
LOC_53                        1.064429
LOC_55                        1.021160
TOA_15                        1.380415
TOA_20                        1.025419
TOA_30                        1.203014
TOA_35                        1.072185
TOA_38                        1.146874
TOA_42                        1.009303
TOA_44                        1.002899
PPGROUP_11                    1.147169

Removed AFTER this step: LOC_40 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8241  -0.7412  -0.1384   0.8202   3.1926  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.91995    0.19433  15.026  < 2e-16 ***
GSEGRD                        -0.53838    0.11465  -4.696 2.65e-06 ***
LowerLimitAge                 -2.00819    0.11766 -17.068  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98832    0.08782  11.254  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13622    0.09362   1.455 0.145683    
BLS_FEDERAL_JobOpenings_Level  0.61938    0.06646   9.319  < 2e-16 ***
LOSSqrt                       -6.57235    0.18449 -35.623  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21344    0.10501  -2.033 0.042100 *  
AGELVL_B                      -0.58189    0.21940  -2.652 0.007996 ** 
AGELVL_C                      -0.16813    0.08522  -1.973 0.048507 *  
AGELVL_E                       0.27695    0.06720   4.122 3.76e-05 ***
AGELVL_F                       0.49463    0.06993   7.073 1.52e-12 ***
AGELVL_G                       0.71368    0.07380   9.670  < 2e-16 ***
AGELVL_H                       0.85002    0.07608  11.173  < 2e-16 ***
AGELVL_I                       0.71879    0.08097   8.878  < 2e-16 ***
LOC_01                        -0.24017    0.16493  -1.456 0.145348    
LOC_04                         0.49380    0.13343   3.701 0.000215 ***
LOC_06                         0.38911    0.08329   4.672 2.99e-06 ***
LOC_08                         0.11584    0.12241   0.946 0.343994    
LOC_13                         0.14005    0.11201   1.250 0.211173    
LOC_17                        -0.24761    0.14159  -1.749 0.080320 .  
LOC_18                        -0.26030    0.24861  -1.047 0.295095    
LOC_20                         0.58182    0.25329   2.297 0.021617 *  
LOC_24                        -0.13564    0.07917  -1.713 0.086657 .  
LOC_27                         0.25851    0.23503   1.100 0.271375    
LOC_29                        -0.38079    0.16593  -2.295 0.021738 *  
LOC_30                         0.64770    0.23726   2.730 0.006336 ** 
LOC_34                        -0.20443    0.19284  -1.060 0.289091    
LOC_35                         0.43879    0.14756   2.974 0.002943 ** 
LOC_38                         0.45560    0.36450   1.250 0.211316    
LOC_39                        -0.31764    0.13936  -2.279 0.022645 *  
LOC_42                        -0.32512    0.14053  -2.314 0.020694 *  
LOC_46                         0.78137    0.25103   3.113 0.001854 ** 
LOC_47                        -0.21214    0.20697  -1.025 0.305373    
LOC_48                         0.22344    0.08828   2.531 0.011374 *  
LOC_49                        -0.21533    0.20585  -1.046 0.295539    
LOC_51                        -0.17933    0.07431  -2.413 0.015806 *  
LOC_53                         0.37093    0.12489   2.970 0.002977 ** 
LOC_55                        -0.35054    0.24785  -1.414 0.157262    
TOA_15                         0.07065    0.06846   1.032 0.302079    
TOA_20                         1.12922    0.19254   5.865 4.50e-09 ***
TOA_30                         0.34544    0.08750   3.948 7.88e-05 ***
TOA_35                        -0.54874    0.28587  -1.920 0.054918 .  
TOA_38                         0.33459    0.06633   5.044 4.56e-07 ***
TOA_42                         1.14032    0.44108   2.585 0.009729 ** 
TOA_44                         2.14743    1.07161   2.004 0.045077 *  
PPGROUP_11                    -0.40830    0.11651  -3.504 0.000457 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14551  on 14873  degrees of freedom
AIC: 14645

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.474094
LowerLimitAge                 2.246894
BLS_FEDERAL_OtherSep_Rate     1.340107
BLS_FEDERAL_Quits_Rate        1.210536
BLS_FEDERAL_JobOpenings_Level 1.088166
LOSSqrt                       1.688079
SEPCount_EFDATE_OCCLog        1.094754
AGELVL_B                      1.162661
AGELVL_C                      1.657351
AGELVL_E                      1.402507
AGELVL_F                      1.278284
AGELVL_G                      1.264822
AGELVL_H                      1.356156
AGELVL_I                      1.512463
LOC_01                        1.031813
LOC_04                        1.068566
LOC_06                        1.126183
LOC_08                        1.058826
LOC_13                        1.070850
LOC_17                        1.040736
LOC_18                        1.018376
LOC_20                        1.016253
LOC_24                        1.170786
LOC_27                        1.018829
LOC_29                        1.034399
LOC_30                        1.024005
LOC_34                        1.026644
LOC_35                        1.054978
LOC_38                        1.010541
LOC_39                        1.045264
LOC_42                        1.045865
LOC_46                        1.023603
LOC_47                        1.021623
LOC_48                        1.126688
LOC_49                        1.021417
LOC_51                        1.171317
LOC_53                        1.059714
LOC_55                        1.020241
TOA_15                        1.380371
TOA_20                        1.025235
TOA_30                        1.202260
TOA_35                        1.072083
TOA_38                        1.145634
TOA_42                        1.009265
TOA_44                        1.002866
PPGROUP_11                    1.146138

Removed AFTER this step: LOC_08 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8251  -0.7410  -0.1385   0.8202   3.1898  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.93071    0.19400  15.107  < 2e-16 ***
GSEGRD                        -0.54239    0.11457  -4.734 2.20e-06 ***
LowerLimitAge                 -2.00753    0.11766 -17.062  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98768    0.08781  11.248  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13735    0.09361   1.467 0.142289    
BLS_FEDERAL_JobOpenings_Level  0.61893    0.06646   9.313  < 2e-16 ***
LOSSqrt                       -6.57035    0.18446 -35.619  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.21170    0.10499  -2.016 0.043756 *  
AGELVL_B                      -0.58558    0.21937  -2.669 0.007599 ** 
AGELVL_C                      -0.16903    0.08522  -1.984 0.047308 *  
AGELVL_E                       0.27679    0.06719   4.119 3.80e-05 ***
AGELVL_F                       0.49422    0.06993   7.068 1.58e-12 ***
AGELVL_G                       0.71464    0.07381   9.683  < 2e-16 ***
AGELVL_H                       0.85074    0.07605  11.186  < 2e-16 ***
AGELVL_I                       0.71829    0.08097   8.871  < 2e-16 ***
LOC_01                        -0.24936    0.16464  -1.515 0.129867    
LOC_04                         0.48373    0.13300   3.637 0.000276 ***
LOC_06                         0.37977    0.08270   4.592 4.39e-06 ***
LOC_13                         0.13049    0.11155   1.170 0.242078    
LOC_17                        -0.25680    0.14124  -1.818 0.069043 .  
LOC_18                        -0.26923    0.24841  -1.084 0.278452    
LOC_20                         0.57186    0.25308   2.260 0.023845 *  
LOC_24                        -0.14473    0.07859  -1.842 0.065524 .  
LOC_27                         0.24897    0.23480   1.060 0.288988    
LOC_29                        -0.39034    0.16562  -2.357 0.018431 *  
LOC_30                         0.63746    0.23702   2.690 0.007156 ** 
LOC_34                        -0.21333    0.19260  -1.108 0.268013    
LOC_35                         0.42872    0.14717   2.913 0.003580 ** 
LOC_38                         0.44564    0.36434   1.223 0.221273    
LOC_39                        -0.32682    0.13901  -2.351 0.018720 *  
LOC_42                        -0.33414    0.14020  -2.383 0.017158 *  
LOC_46                         0.77098    0.25078   3.074 0.002110 ** 
LOC_47                        -0.22145    0.20672  -1.071 0.284043    
LOC_48                         0.21372    0.08768   2.437 0.014789 *  
LOC_49                        -0.22487    0.20560  -1.094 0.274093    
LOC_51                        -0.18872    0.07365  -2.562 0.010396 *  
LOC_53                         0.36128    0.12447   2.903 0.003702 ** 
LOC_55                        -0.35954    0.24765  -1.452 0.146550    
TOA_15                         0.07177    0.06845   1.048 0.294451    
TOA_20                         1.13161    0.19256   5.877 4.19e-09 ***
TOA_30                         0.34259    0.08746   3.917 8.96e-05 ***
TOA_35                        -0.55267    0.28587  -1.933 0.053204 .  
TOA_38                         0.33262    0.06630   5.017 5.25e-07 ***
TOA_42                         1.13697    0.44108   2.578 0.009946 ** 
TOA_44                         2.13942    1.07158   1.997 0.045878 *  
PPGROUP_11                    -0.40920    0.11651  -3.512 0.000444 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14552  on 14874  degrees of freedom
AIC: 14644

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.471910
LowerLimitAge                 2.246864
BLS_FEDERAL_OtherSep_Rate     1.339985
BLS_FEDERAL_Quits_Rate        1.210308
BLS_FEDERAL_JobOpenings_Level 1.088069
LOSSqrt                       1.687659
SEPCount_EFDATE_OCCLog        1.094374
AGELVL_B                      1.162369
AGELVL_C                      1.657200
AGELVL_E                      1.402616
AGELVL_F                      1.278362
AGELVL_G                      1.264575
AGELVL_H                      1.356430
AGELVL_I                      1.512358
LOC_01                        1.028245
LOC_04                        1.061789
LOC_06                        1.110438
LOC_13                        1.062187
LOC_17                        1.035886
LOC_18                        1.016929
LOC_20                        1.014500
LOC_24                        1.153692
LOC_27                        1.016974
LOC_29                        1.030603
LOC_30                        1.021874
LOC_34                        1.024240
LOC_35                        1.049512
LOC_38                        1.009706
LOC_39                        1.040243
LOC_42                        1.041116
LOC_46                        1.021645
LOC_47                        1.019330
LOC_48                        1.111513
LOC_49                        1.018985
LOC_51                        1.150611
LOC_53                        1.052689
LOC_55                        1.018762
TOA_15                        1.379999
TOA_20                        1.025042
TOA_30                        1.200666
TOA_35                        1.071887
TOA_38                        1.144507
TOA_42                        1.009210
TOA_44                        1.002803
PPGROUP_11                    1.146013

Removed AFTER this step: TOA_15 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8323  -0.7406  -0.1371   0.8196   3.1978  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.97986    0.18830  15.825  < 2e-16 ***
GSEGRD                        -0.55831    0.11351  -4.918 8.72e-07 ***
LowerLimitAge                 -1.99798    0.11727 -17.038  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98819    0.08783  11.251  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13718    0.09359   1.466 0.142725    
BLS_FEDERAL_JobOpenings_Level  0.61929    0.06644   9.321  < 2e-16 ***
LOSSqrt                       -6.64881    0.16895 -39.355  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20793    0.10489  -1.982 0.047433 *  
AGELVL_B                      -0.56573    0.21838  -2.591 0.009582 ** 
AGELVL_C                      -0.16842    0.08516  -1.978 0.047963 *  
AGELVL_E                       0.27815    0.06715   4.142 3.44e-05 ***
AGELVL_F                       0.49605    0.06988   7.099 1.26e-12 ***
AGELVL_G                       0.71697    0.07378   9.718  < 2e-16 ***
AGELVL_H                       0.85256    0.07607  11.208  < 2e-16 ***
AGELVL_I                       0.72159    0.08097   8.912  < 2e-16 ***
LOC_01                        -0.25083    0.16473  -1.523 0.127830    
LOC_04                         0.48475    0.13291   3.647 0.000265 ***
LOC_06                         0.37832    0.08272   4.573 4.80e-06 ***
LOC_13                         0.12905    0.11152   1.157 0.247190    
LOC_17                        -0.25997    0.14123  -1.841 0.065651 .  
LOC_18                        -0.26633    0.24836  -1.072 0.283562    
LOC_20                         0.57661    0.25296   2.279 0.022642 *  
LOC_24                        -0.14327    0.07855  -1.824 0.068170 .  
LOC_27                         0.24864    0.23477   1.059 0.289580    
LOC_29                        -0.39332    0.16562  -2.375 0.017557 *  
LOC_30                         0.63566    0.23706   2.681 0.007330 ** 
LOC_34                        -0.21444    0.19270  -1.113 0.265779    
LOC_35                         0.42921    0.14711   2.918 0.003528 ** 
LOC_38                         0.44493    0.36418   1.222 0.221813    
LOC_39                        -0.32672    0.13904  -2.350 0.018777 *  
LOC_42                        -0.33598    0.14021  -2.396 0.016565 *  
LOC_46                         0.76916    0.25072   3.068 0.002156 ** 
LOC_47                        -0.22347    0.20673  -1.081 0.279714    
LOC_48                         0.21215    0.08767   2.420 0.015530 *  
LOC_49                        -0.22372    0.20539  -1.089 0.276056    
LOC_51                        -0.18584    0.07353  -2.528 0.011485 *  
LOC_53                         0.36059    0.12442   2.898 0.003754 ** 
LOC_55                        -0.36247    0.24774  -1.463 0.143450    
TOA_20                         1.11130    0.19186   5.792 6.95e-09 ***
TOA_30                         0.33321    0.08714   3.824 0.000131 ***
TOA_35                        -0.59029    0.28374  -2.080 0.037492 *  
TOA_38                         0.31558    0.06437   4.902 9.47e-07 ***
TOA_42                         1.11605    0.44066   2.533 0.011319 *  
TOA_44                         2.13357    1.07174   1.991 0.046508 *  
PPGROUP_11                    -0.41853    0.11610  -3.605 0.000312 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14553  on 14875  degrees of freedom
AIC: 14643

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.446463
LowerLimitAge                 2.231811
BLS_FEDERAL_OtherSep_Rate     1.339932
BLS_FEDERAL_Quits_Rate        1.210258
BLS_FEDERAL_JobOpenings_Level 1.088234
LOSSqrt                       1.412967
SEPCount_EFDATE_OCCLog        1.093163
AGELVL_B                      1.153319
AGELVL_C                      1.657331
AGELVL_E                      1.401872
AGELVL_F                      1.277556
AGELVL_G                      1.263549
AGELVL_H                      1.355802
AGELVL_I                      1.509972
LOC_01                        1.028137
LOC_04                        1.061851
LOC_06                        1.110070
LOC_13                        1.062088
LOC_17                        1.035430
LOC_18                        1.016804
LOC_20                        1.014193
LOC_24                        1.153416
LOC_27                        1.016966
LOC_29                        1.030312
LOC_30                        1.021783
LOC_34                        1.024156
LOC_35                        1.049494
LOC_38                        1.009721
LOC_39                        1.040222
LOC_42                        1.040943
LOC_46                        1.021593
LOC_47                        1.019240
LOC_48                        1.111186
LOC_49                        1.019008
LOC_51                        1.149432
LOC_53                        1.052728
LOC_55                        1.018643
TOA_20                        1.014651
TOA_30                        1.187990
TOA_35                        1.054678
TOA_38                        1.075975
TOA_42                        1.007166
TOA_44                        1.002774
PPGROUP_11                    1.139765

Removed AFTER this step: LOC_27 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8336  -0.7410  -0.1372   0.8201   3.1970  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.99036    0.18804  15.903  < 2e-16 ***
GSEGRD                        -0.56465    0.11336  -4.981 6.32e-07 ***
LowerLimitAge                 -2.00011    0.11725 -17.059  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98870    0.08783  11.257  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13684    0.09359   1.462 0.143692    
BLS_FEDERAL_JobOpenings_Level  0.61996    0.06643   9.333  < 2e-16 ***
LOSSqrt                       -6.64595    0.16890 -39.348  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20831    0.10487  -1.986 0.046994 *  
AGELVL_B                      -0.56567    0.21833  -2.591 0.009573 ** 
AGELVL_C                      -0.16999    0.08515  -1.996 0.045886 *  
AGELVL_E                       0.27791    0.06714   4.139 3.49e-05 ***
AGELVL_F                       0.49628    0.06987   7.103 1.22e-12 ***
AGELVL_G                       0.71713    0.07377   9.721  < 2e-16 ***
AGELVL_H                       0.85276    0.07607  11.211  < 2e-16 ***
AGELVL_I                       0.72153    0.08097   8.911  < 2e-16 ***
LOC_01                        -0.25607    0.16468  -1.555 0.119953    
LOC_04                         0.47922    0.13282   3.608 0.000308 ***
LOC_06                         0.37301    0.08258   4.517 6.28e-06 ***
LOC_13                         0.12394    0.11142   1.112 0.265980    
LOC_17                        -0.26513    0.14115  -1.878 0.060342 .  
LOC_18                        -0.27207    0.24833  -1.096 0.273256    
LOC_20                         0.57121    0.25294   2.258 0.023926 *  
LOC_24                        -0.14771    0.07845  -1.883 0.059720 .  
LOC_29                        -0.39902    0.16555  -2.410 0.015941 *  
LOC_30                         0.62986    0.23701   2.658 0.007872 ** 
LOC_34                        -0.21964    0.19265  -1.140 0.254251    
LOC_35                         0.42372    0.14704   2.882 0.003955 ** 
LOC_38                         0.43907    0.36417   1.206 0.227945    
LOC_39                        -0.33221    0.13895  -2.391 0.016814 *  
LOC_42                        -0.34125    0.14013  -2.435 0.014885 *  
LOC_46                         0.76314    0.25067   3.044 0.002332 ** 
LOC_47                        -0.22911    0.20668  -1.108 0.267648    
LOC_48                         0.20688    0.08754   2.363 0.018111 *  
LOC_49                        -0.22913    0.20535  -1.116 0.264507    
LOC_51                        -0.19078    0.07339  -2.600 0.009334 ** 
LOC_53                         0.35513    0.12433   2.856 0.004284 ** 
LOC_55                        -0.36864    0.24770  -1.488 0.136686    
TOA_20                         1.11161    0.19192   5.792 6.95e-09 ***
TOA_30                         0.33220    0.08715   3.812 0.000138 ***
TOA_35                        -0.59468    0.28373  -2.096 0.036089 *  
TOA_38                         0.31745    0.06434   4.934 8.04e-07 ***
TOA_42                         1.11427    0.44069   2.528 0.011456 *  
TOA_44                         2.13068    1.07174   1.988 0.046804 *  
PPGROUP_11                    -0.42003    0.11610  -3.618 0.000297 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14554  on 14876  degrees of freedom
AIC: 14642

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.442709
LowerLimitAge                 2.231148
BLS_FEDERAL_OtherSep_Rate     1.339987
BLS_FEDERAL_Quits_Rate        1.210271
BLS_FEDERAL_JobOpenings_Level 1.088134
LOSSqrt                       1.412203
SEPCount_EFDATE_OCCLog        1.093173
AGELVL_B                      1.153381
AGELVL_C                      1.656663
AGELVL_E                      1.401770
AGELVL_F                      1.277569
AGELVL_G                      1.263577
AGELVL_H                      1.355800
AGELVL_I                      1.509930
LOC_01                        1.027220
LOC_04                        1.060244
LOC_06                        1.106053
LOC_13                        1.060145
LOC_17                        1.034230
LOC_18                        1.016329
LOC_20                        1.013785
LOC_24                        1.150238
LOC_29                        1.029246
LOC_30                        1.021240
LOC_34                        1.023515
LOC_35                        1.048198
LOC_38                        1.009493
LOC_39                        1.038804
LOC_42                        1.039667
LOC_46                        1.021069
LOC_47                        1.018572
LOC_48                        1.107684
LOC_49                        1.018392
LOC_51                        1.144940
LOC_53                        1.050951
LOC_55                        1.018087
TOA_20                        1.014639
TOA_30                        1.187852
TOA_35                        1.054407
TOA_38                        1.075292
TOA_42                        1.007154
TOA_44                        1.002767
PPGROUP_11                    1.139572

Removed AFTER this step: LOC_18 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8336  -0.7405  -0.1369   0.8203   3.1984  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.98884    0.18805  15.894  < 2e-16 ***
GSEGRD                        -0.56331    0.11335  -4.970 6.71e-07 ***
LowerLimitAge                 -2.00158    0.11724 -17.072  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98880    0.08783  11.258  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13474    0.09356   1.440 0.149811    
BLS_FEDERAL_JobOpenings_Level  0.61948    0.06642   9.326  < 2e-16 ***
LOSSqrt                       -6.64747    0.16888 -39.362  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20853    0.10487  -1.988 0.046757 *  
AGELVL_B                      -0.56412    0.21831  -2.584 0.009764 ** 
AGELVL_C                      -0.17122    0.08514  -2.011 0.044313 *  
AGELVL_E                       0.27821    0.06714   4.144 3.42e-05 ***
AGELVL_F                       0.49623    0.06987   7.102 1.23e-12 ***
AGELVL_G                       0.71901    0.07375   9.749  < 2e-16 ***
AGELVL_H                       0.85442    0.07604  11.236  < 2e-16 ***
AGELVL_I                       0.72236    0.08097   8.921  < 2e-16 ***
LOC_01                        -0.25115    0.16461  -1.526 0.127061    
LOC_04                         0.48395    0.13275   3.646 0.000267 ***
LOC_06                         0.37794    0.08246   4.584 4.57e-06 ***
LOC_13                         0.12842    0.11135   1.153 0.248773    
LOC_17                        -0.26047    0.14108  -1.846 0.064859 .  
LOC_20                         0.57591    0.25290   2.277 0.022771 *  
LOC_24                        -0.14350    0.07835  -1.832 0.067022 .  
LOC_29                        -0.39398    0.16547  -2.381 0.017271 *  
LOC_30                         0.63460    0.23698   2.678 0.007410 ** 
LOC_34                        -0.21472    0.19259  -1.115 0.264888    
LOC_35                         0.42841    0.14698   2.915 0.003559 ** 
LOC_38                         0.44388    0.36413   1.219 0.222837    
LOC_39                        -0.32712    0.13887  -2.356 0.018493 *  
LOC_42                        -0.33635    0.14006  -2.401 0.016329 *  
LOC_46                         0.76816    0.25064   3.065 0.002179 ** 
LOC_47                        -0.22387    0.20661  -1.084 0.278562    
LOC_48                         0.21159    0.08743   2.420 0.015519 *  
LOC_49                        -0.22425    0.20529  -1.092 0.274669    
LOC_51                        -0.18649    0.07328  -2.545 0.010933 *  
LOC_53                         0.35985    0.12425   2.896 0.003778 ** 
LOC_55                        -0.36270    0.24761  -1.465 0.142970    
TOA_20                         1.11140    0.19191   5.791 6.99e-09 ***
TOA_30                         0.33374    0.08714   3.830 0.000128 ***
TOA_35                        -0.59297    0.28370  -2.090 0.036609 *  
TOA_38                         0.31366    0.06423   4.883 1.04e-06 ***
TOA_42                         1.11548    0.44067   2.531 0.011364 *  
TOA_44                         2.13439    1.07172   1.992 0.046420 *  
PPGROUP_11                    -0.42149    0.11611  -3.630 0.000283 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14555  on 14877  degrees of freedom
AIC: 14641

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.442606
LowerLimitAge                 2.231047
BLS_FEDERAL_OtherSep_Rate     1.339971
BLS_FEDERAL_Quits_Rate        1.209863
BLS_FEDERAL_JobOpenings_Level 1.088031
LOSSqrt                       1.412203
SEPCount_EFDATE_OCCLog        1.093142
AGELVL_B                      1.153281
AGELVL_C                      1.656357
AGELVL_E                      1.401727
AGELVL_F                      1.277552
AGELVL_G                      1.262980
AGELVL_H                      1.355447
AGELVL_I                      1.509768
LOC_01                        1.026454
LOC_04                        1.059092
LOC_06                        1.102722
LOC_13                        1.058690
LOC_17                        1.033273
LOC_20                        1.013485
LOC_24                        1.147435
LOC_29                        1.028436
LOC_30                        1.020893
LOC_34                        1.022944
LOC_35                        1.047286
LOC_38                        1.009342
LOC_39                        1.037628
LOC_42                        1.038581
LOC_46                        1.020717
LOC_47                        1.018020
LOC_48                        1.104957
LOC_49                        1.017906
LOC_51                        1.141582
LOC_53                        1.049660
LOC_55                        1.017595
TOA_20                        1.014600
TOA_30                        1.187619
TOA_35                        1.054347
TOA_38                        1.072010
TOA_42                        1.007143
TOA_44                        1.002758
PPGROUP_11                    1.139384

Removed AFTER this step: LOC_47 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8334  -0.7413  -0.1372   0.8200   3.2002  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.97961    0.18782  15.864  < 2e-16 ***
GSEGRD                        -0.55978    0.11330  -4.941 7.78e-07 ***
LowerLimitAge                 -2.00380    0.11723 -17.092  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98989    0.08782  11.272  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13394    0.09354   1.432 0.152205    
BLS_FEDERAL_JobOpenings_Level  0.62000    0.06642   9.335  < 2e-16 ***
LOSSqrt                       -6.64794    0.16886 -39.369  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20856    0.10486  -1.989 0.046714 *  
AGELVL_B                      -0.56489    0.21823  -2.588 0.009642 ** 
AGELVL_C                      -0.17035    0.08513  -2.001 0.045379 *  
AGELVL_E                       0.27891    0.06714   4.154 3.26e-05 ***
AGELVL_F                       0.49647    0.06986   7.107 1.19e-12 ***
AGELVL_G                       0.72011    0.07374   9.765  < 2e-16 ***
AGELVL_H                       0.85602    0.07602  11.260  < 2e-16 ***
AGELVL_I                       0.72302    0.08097   8.929  < 2e-16 ***
LOC_01                        -0.24562    0.16452  -1.493 0.135455    
LOC_04                         0.49016    0.13263   3.696 0.000219 ***
LOC_06                         0.38361    0.08229   4.662 3.14e-06 ***
LOC_13                         0.13400    0.11123   1.205 0.228304    
LOC_17                        -0.25504    0.14099  -1.809 0.070465 .  
LOC_20                         0.58183    0.25284   2.301 0.021381 *  
LOC_24                        -0.13835    0.07821  -1.769 0.076882 .  
LOC_29                        -0.38811    0.16538  -2.347 0.018939 *  
LOC_30                         0.64096    0.23691   2.705 0.006821 ** 
LOC_34                        -0.20934    0.19253  -1.087 0.276905    
LOC_35                         0.43448    0.14687   2.958 0.003094 ** 
LOC_38                         0.45001    0.36409   1.236 0.216463    
LOC_39                        -0.32133    0.13877  -2.316 0.020579 *  
LOC_42                        -0.33085    0.13997  -2.364 0.018090 *  
LOC_46                         0.77457    0.25059   3.091 0.001995 ** 
LOC_48                         0.21744    0.08727   2.492 0.012717 *  
LOC_49                        -0.21861    0.20523  -1.065 0.286769    
LOC_51                        -0.18121    0.07311  -2.478 0.013196 *  
LOC_53                         0.36553    0.12415   2.944 0.003237 ** 
LOC_55                        -0.35649    0.24754  -1.440 0.149828    
TOA_20                         1.11193    0.19197   5.792 6.94e-09 ***
TOA_30                         0.33483    0.08713   3.843 0.000122 ***
TOA_35                        -0.58976    0.28367  -2.079 0.037611 *  
TOA_38                         0.31220    0.06422   4.862 1.16e-06 ***
TOA_42                         1.11739    0.44066   2.536 0.011222 *  
TOA_44                         2.13834    1.07172   1.995 0.046016 *  
PPGROUP_11                    -0.41945    0.11608  -3.613 0.000302 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14556  on 14878  degrees of freedom
AIC: 14640

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.441478
LowerLimitAge                 2.230723
BLS_FEDERAL_OtherSep_Rate     1.339728
BLS_FEDERAL_Quits_Rate        1.209663
BLS_FEDERAL_JobOpenings_Level 1.088000
LOSSqrt                       1.412143
SEPCount_EFDATE_OCCLog        1.093141
AGELVL_B                      1.153341
AGELVL_C                      1.656215
AGELVL_E                      1.401687
AGELVL_F                      1.277689
AGELVL_G                      1.262870
AGELVL_H                      1.355321
AGELVL_I                      1.510002
LOC_01                        1.025462
LOC_04                        1.057098
LOC_06                        1.098240
LOC_13                        1.056395
LOC_17                        1.031951
LOC_20                        1.013009
LOC_24                        1.143145
LOC_29                        1.027318
LOC_30                        1.020271
LOC_34                        1.022244
LOC_35                        1.045751
LOC_38                        1.009095
LOC_39                        1.036075
LOC_42                        1.037193
LOC_46                        1.020145
LOC_48                        1.100711
LOC_49                        1.017242
LOC_51                        1.136432
LOC_53                        1.047766
LOC_55                        1.017044
TOA_20                        1.014611
TOA_30                        1.187469
TOA_35                        1.054241
TOA_38                        1.071490
TOA_42                        1.007120
TOA_44                        1.002746
PPGROUP_11                    1.139105

Removed AFTER this step: LOC_49 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8326  -0.7416  -0.1372   0.8194   3.2019  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.97290    0.18770  15.839  < 2e-16 ***
GSEGRD                        -0.55738    0.11327  -4.921 8.62e-07 ***
LowerLimitAge                 -2.00453    0.11722 -17.101  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98984    0.08782  11.272  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13534    0.09354   1.447 0.147900    
BLS_FEDERAL_JobOpenings_Level  0.61987    0.06642   9.333  < 2e-16 ***
LOSSqrt                       -6.64867    0.16886 -39.374  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20778    0.10486  -1.982 0.047529 *  
AGELVL_B                      -0.56304    0.21822  -2.580 0.009875 ** 
AGELVL_C                      -0.17159    0.08512  -2.016 0.043810 *  
AGELVL_E                       0.27819    0.06713   4.144 3.41e-05 ***
AGELVL_F                       0.49626    0.06986   7.103 1.22e-12 ***
AGELVL_G                       0.72011    0.07374   9.765  < 2e-16 ***
AGELVL_H                       0.85556    0.07601  11.256  < 2e-16 ***
AGELVL_I                       0.72165    0.08095   8.914  < 2e-16 ***
LOC_01                        -0.24016    0.16445  -1.460 0.144177    
LOC_04                         0.49580    0.13253   3.741 0.000183 ***
LOC_06                         0.38909    0.08213   4.737 2.16e-06 ***
LOC_13                         0.13946    0.11111   1.255 0.209439    
LOC_17                        -0.24971    0.14091  -1.772 0.076368 .  
LOC_20                         0.58749    0.25280   2.324 0.020128 *  
LOC_24                        -0.13315    0.07805  -1.706 0.088030 .  
LOC_29                        -0.38242    0.16529  -2.314 0.020693 *  
LOC_30                         0.64687    0.23686   2.731 0.006314 ** 
LOC_34                        -0.20393    0.19246  -1.060 0.289340    
LOC_35                         0.44006    0.14678   2.998 0.002717 ** 
LOC_38                         0.45583    0.36407   1.252 0.210554    
LOC_39                        -0.31597    0.13868  -2.278 0.022700 *  
LOC_42                        -0.32559    0.13988  -2.328 0.019932 *  
LOC_46                         0.78028    0.25053   3.114 0.001843 ** 
LOC_48                         0.22292    0.08712   2.559 0.010502 *  
LOC_51                        -0.17580    0.07293  -2.410 0.015931 *  
LOC_53                         0.37117    0.12404   2.992 0.002768 ** 
LOC_55                        -0.35099    0.24749  -1.418 0.156124    
TOA_20                         1.11084    0.19202   5.785 7.25e-09 ***
TOA_30                         0.33748    0.08710   3.875 0.000107 ***
TOA_35                        -0.58704    0.28364  -2.070 0.038482 *  
TOA_38                         0.31253    0.06422   4.867 1.13e-06 ***
TOA_42                         1.11974    0.44065   2.541 0.011050 *  
TOA_44                         2.14300    1.07173   2.000 0.045546 *  
PPGROUP_11                    -0.41996    0.11607  -3.618 0.000297 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14557  on 14879  degrees of freedom
AIC: 14639

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.440964
LowerLimitAge                 2.230539
BLS_FEDERAL_OtherSep_Rate     1.339622
BLS_FEDERAL_Quits_Rate        1.209313
BLS_FEDERAL_JobOpenings_Level 1.088000
LOSSqrt                       1.412197
SEPCount_EFDATE_OCCLog        1.093097
AGELVL_B                      1.153190
AGELVL_C                      1.655688
AGELVL_E                      1.401531
AGELVL_F                      1.277535
AGELVL_G                      1.262762
AGELVL_H                      1.355273
AGELVL_I                      1.509508
LOC_01                        1.024460
LOC_04                        1.055396
LOC_06                        1.093927
LOC_13                        1.054135
LOC_17                        1.030631
LOC_20                        1.012557
LOC_24                        1.138629
LOC_29                        1.026235
LOC_30                        1.019709
LOC_34                        1.021518
LOC_35                        1.044403
LOC_38                        1.008862
LOC_39                        1.034693
LOC_42                        1.035878
LOC_46                        1.019673
LOC_48                        1.096847
LOC_51                        1.130880
LOC_53                        1.045840
LOC_55                        1.016591
TOA_20                        1.014529
TOA_30                        1.186588
TOA_35                        1.054118
TOA_38                        1.071478
TOA_42                        1.007089
TOA_44                        1.002729
PPGROUP_11                    1.139079

Removed AFTER this step: LOC_34 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8326  -0.7421  -0.1372   0.8190   3.2036  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.96643    0.18756  15.816  < 2e-16 ***
GSEGRD                        -0.55804    0.11327  -4.927 8.37e-07 ***
LowerLimitAge                 -2.00321    0.11721 -17.091  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98923    0.08781  11.265  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13544    0.09353   1.448 0.147618    
BLS_FEDERAL_JobOpenings_Level  0.62000    0.06641   9.336  < 2e-16 ***
LOSSqrt                       -6.65061    0.16884 -39.390  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20682    0.10485  -1.973 0.048551 *  
AGELVL_B                      -0.56907    0.21803  -2.610 0.009053 ** 
AGELVL_C                      -0.17488    0.08504  -2.056 0.039736 *  
AGELVL_E                       0.27858    0.06713   4.150 3.33e-05 ***
AGELVL_F                       0.49575    0.06986   7.097 1.28e-12 ***
AGELVL_G                       0.72038    0.07374   9.769  < 2e-16 ***
AGELVL_H                       0.85550    0.07601  11.255  < 2e-16 ***
AGELVL_I                       0.72202    0.08096   8.918  < 2e-16 ***
LOC_01                        -0.23449    0.16435  -1.427 0.153645    
LOC_04                         0.50092    0.13244   3.782 0.000155 ***
LOC_06                         0.39454    0.08196   4.814 1.48e-06 ***
LOC_13                         0.14482    0.11099   1.305 0.191963    
LOC_17                        -0.24419    0.14080  -1.734 0.082862 .  
LOC_20                         0.59271    0.25274   2.345 0.019019 *  
LOC_24                        -0.12742    0.07786  -1.637 0.101719    
LOC_29                        -0.37706    0.16521  -2.282 0.022469 *  
LOC_30                         0.65192    0.23681   2.753 0.005907 ** 
LOC_35                         0.44538    0.14668   3.036 0.002395 ** 
LOC_38                         0.46117    0.36405   1.267 0.205226    
LOC_39                        -0.31031    0.13856  -2.240 0.025120 *  
LOC_42                        -0.31973    0.13976  -2.288 0.022154 *  
LOC_46                         0.78532    0.25049   3.135 0.001718 ** 
LOC_48                         0.22815    0.08697   2.623 0.008708 ** 
LOC_51                        -0.17013    0.07273  -2.339 0.019321 *  
LOC_53                         0.37691    0.12391   3.042 0.002351 ** 
LOC_55                        -0.34535    0.24742  -1.396 0.162761    
TOA_20                         1.11053    0.19195   5.785 7.23e-09 ***
TOA_30                         0.33988    0.08706   3.904 9.46e-05 ***
TOA_35                        -0.59308    0.28349  -2.092 0.036434 *  
TOA_38                         0.31228    0.06421   4.863 1.15e-06 ***
TOA_42                         1.12173    0.44062   2.546 0.010903 *  
TOA_44                         2.14862    1.07171   2.005 0.044980 *  
PPGROUP_11                    -0.41872    0.11603  -3.609 0.000308 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14559  on 14880  degrees of freedom
AIC: 14639

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.441092
LowerLimitAge                 2.230622
BLS_FEDERAL_OtherSep_Rate     1.339593
BLS_FEDERAL_Quits_Rate        1.209410
BLS_FEDERAL_JobOpenings_Level 1.087977
LOSSqrt                       1.412039
SEPCount_EFDATE_OCCLog        1.093007
AGELVL_B                      1.152407
AGELVL_C                      1.653729
AGELVL_E                      1.401295
AGELVL_F                      1.277467
AGELVL_G                      1.262676
AGELVL_H                      1.355241
AGELVL_I                      1.509331
LOC_01                        1.023371
LOC_04                        1.053965
LOC_06                        1.089602
LOC_13                        1.051921
LOC_17                        1.029202
LOC_20                        1.012167
LOC_24                        1.133096
LOC_29                        1.025262
LOC_30                        1.019297
LOC_35                        1.043170
LOC_38                        1.008663
LOC_39                        1.033141
LOC_42                        1.034230
LOC_46                        1.019298
LOC_48                        1.093275
LOC_51                        1.124673
LOC_53                        1.043836
LOC_55                        1.016109
TOA_20                        1.014544
TOA_30                        1.185853
TOA_35                        1.053524
TOA_38                        1.071444
TOA_42                        1.007066
TOA_44                        1.002704
PPGROUP_11                    1.138933

Removed AFTER this step: LOC_38 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8331  -0.7421  -0.1371   0.8188   3.2035  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.97818    0.18732  15.898  < 2e-16 ***
GSEGRD                        -0.56519    0.11312  -4.996 5.85e-07 ***
LowerLimitAge                 -2.00124    0.11718 -17.078  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98917    0.08781  11.265  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13710    0.09352   1.466 0.142635    
BLS_FEDERAL_JobOpenings_Level  0.61996    0.06641   9.335  < 2e-16 ***
LOSSqrt                       -6.65020    0.16880 -39.397  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20999    0.10482  -2.003 0.045144 *  
AGELVL_B                      -0.57184    0.21803  -2.623 0.008723 ** 
AGELVL_C                      -0.17432    0.08504  -2.050 0.040375 *  
AGELVL_E                       0.27911    0.06712   4.158 3.21e-05 ***
AGELVL_F                       0.49598    0.06985   7.101 1.24e-12 ***
AGELVL_G                       0.72181    0.07373   9.790  < 2e-16 ***
AGELVL_H                       0.85526    0.07601  11.252  < 2e-16 ***
AGELVL_I                       0.72281    0.08094   8.930  < 2e-16 ***
LOC_01                        -0.23807    0.16434  -1.449 0.147438    
LOC_04                         0.49648    0.13239   3.750 0.000177 ***
LOC_06                         0.39078    0.08191   4.771 1.83e-06 ***
LOC_13                         0.14115    0.11095   1.272 0.203317    
LOC_17                        -0.24771    0.14077  -1.760 0.078468 .  
LOC_20                         0.58835    0.25272   2.328 0.019909 *  
LOC_24                        -0.13064    0.07782  -1.679 0.093194 .  
LOC_29                        -0.38088    0.16518  -2.306 0.021122 *  
LOC_30                         0.64699    0.23677   2.733 0.006285 ** 
LOC_35                         0.44103    0.14665   3.007 0.002636 ** 
LOC_39                        -0.31403    0.13853  -2.267 0.023401 *  
LOC_42                        -0.32319    0.13974  -2.313 0.020732 *  
LOC_46                         0.78044    0.25046   3.116 0.001833 ** 
LOC_48                         0.22423    0.08692   2.580 0.009885 ** 
LOC_51                        -0.17368    0.07268  -2.390 0.016870 *  
LOC_53                         0.37285    0.12386   3.010 0.002611 ** 
LOC_55                        -0.34919    0.24738  -1.412 0.158089    
TOA_20                         1.10820    0.19194   5.774 7.76e-09 ***
TOA_30                         0.34030    0.08703   3.910 9.23e-05 ***
TOA_35                        -0.59697    0.28350  -2.106 0.035230 *  
TOA_38                         0.31127    0.06419   4.849 1.24e-06 ***
TOA_42                         1.13703    0.44044   2.582 0.009835 ** 
TOA_44                         2.14801    1.07171   2.004 0.045039 *  
PPGROUP_11                    -0.42255    0.11597  -3.644 0.000269 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14560  on 14881  degrees of freedom
AIC: 14638

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.437401
LowerLimitAge                 2.229757
BLS_FEDERAL_OtherSep_Rate     1.339492
BLS_FEDERAL_Quits_Rate        1.209139
BLS_FEDERAL_JobOpenings_Level 1.087965
LOSSqrt                       1.411618
SEPCount_EFDATE_OCCLog        1.092337
AGELVL_B                      1.152332
AGELVL_C                      1.653555
AGELVL_E                      1.401297
AGELVL_F                      1.277537
AGELVL_G                      1.262409
AGELVL_H                      1.355159
AGELVL_I                      1.509413
LOC_01                        1.023075
LOC_04                        1.053224
LOC_06                        1.088228
LOC_13                        1.051226
LOC_17                        1.028819
LOC_20                        1.011976
LOC_24                        1.131941
LOC_29                        1.024934
LOC_30                        1.019018
LOC_35                        1.042590
LOC_39                        1.032692
LOC_42                        1.033845
LOC_46                        1.019052
LOC_48                        1.091917
LOC_51                        1.123080
LOC_53                        1.043162
LOC_55                        1.015969
TOA_20                        1.014444
TOA_30                        1.185656
TOA_35                        1.053449
TOA_38                        1.071210
TOA_42                        1.006519
TOA_44                        1.002704
PPGROUP_11                    1.138163

Removed AFTER this step: LOC_13 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8328  -0.7414  -0.1372   0.8197   3.1996  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.99347    0.18696  16.011  < 2e-16 ***
GSEGRD                        -0.56642    0.11313  -5.007 5.53e-07 ***
LowerLimitAge                 -2.00038    0.11718 -17.071  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.98866    0.08779  11.261  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13624    0.09350   1.457 0.145083    
BLS_FEDERAL_JobOpenings_Level  0.62066    0.06641   9.346  < 2e-16 ***
LOSSqrt                       -6.64965    0.16877 -39.401  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20614    0.10477  -1.968 0.049118 *  
AGELVL_B                      -0.57185    0.21799  -2.623 0.008709 ** 
AGELVL_C                      -0.17513    0.08504  -2.059 0.039455 *  
AGELVL_E                       0.27960    0.06712   4.165 3.11e-05 ***
AGELVL_F                       0.49632    0.06985   7.106 1.20e-12 ***
AGELVL_G                       0.72212    0.07372   9.796  < 2e-16 ***
AGELVL_H                       0.85545    0.07599  11.257  < 2e-16 ***
AGELVL_I                       0.72339    0.08095   8.937  < 2e-16 ***
LOC_01                        -0.24911    0.16409  -1.518 0.128980    
LOC_04                         0.48415    0.13203   3.667 0.000245 ***
LOC_06                         0.37972    0.08144   4.662 3.13e-06 ***
LOC_17                        -0.25866    0.14049  -1.841 0.065604 .  
LOC_20                         0.57661    0.25255   2.283 0.022419 *  
LOC_24                        -0.14229    0.07728  -1.841 0.065586 .  
LOC_29                        -0.39220    0.16493  -2.378 0.017408 *  
LOC_30                         0.63486    0.23658   2.683 0.007287 ** 
LOC_35                         0.42881    0.14633   2.930 0.003385 ** 
LOC_39                        -0.32508    0.13825  -2.351 0.018701 *  
LOC_42                        -0.33407    0.13946  -2.395 0.016601 *  
LOC_46                         0.76819    0.25026   3.070 0.002144 ** 
LOC_48                         0.21232    0.08641   2.457 0.014006 *  
LOC_51                        -0.18508    0.07213  -2.566 0.010294 *  
LOC_53                         0.36128    0.12353   2.925 0.003447 ** 
LOC_55                        -0.35941    0.24723  -1.454 0.146006    
TOA_20                         1.10852    0.19183   5.779 7.54e-09 ***
TOA_30                         0.33469    0.08693   3.850 0.000118 ***
TOA_35                        -0.60085    0.28347  -2.120 0.034039 *  
TOA_38                         0.30813    0.06413   4.805 1.55e-06 ***
TOA_42                         1.13362    0.44040   2.574 0.010052 *  
TOA_44                         2.13646    1.07166   1.994 0.046196 *  
PPGROUP_11                    -0.42816    0.11592  -3.694 0.000221 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14562  on 14882  degrees of freedom
AIC: 14638

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.437018
LowerLimitAge                 2.229701
BLS_FEDERAL_OtherSep_Rate     1.339322
BLS_FEDERAL_Quits_Rate        1.209083
BLS_FEDERAL_JobOpenings_Level 1.087880
LOSSqrt                       1.411623
SEPCount_EFDATE_OCCLog        1.091343
AGELVL_B                      1.152194
AGELVL_C                      1.653433
AGELVL_E                      1.401295
AGELVL_F                      1.277524
AGELVL_G                      1.262512
AGELVL_H                      1.355362
AGELVL_I                      1.509405
LOC_01                        1.020234
LOC_04                        1.047587
LOC_06                        1.076039
LOC_17                        1.025023
LOC_20                        1.010633
LOC_24                        1.116448
LOC_29                        1.021994
LOC_30                        1.017351
LOC_35                        1.038133
LOC_39                        1.028675
LOC_42                        1.030027
LOC_46                        1.017548
LOC_48                        1.079290
LOC_51                        1.106252
LOC_53                        1.037582
LOC_55                        1.014920
TOA_20                        1.014397
TOA_30                        1.182600
TOA_35                        1.053292
TOA_38                        1.069624
TOA_42                        1.006494
TOA_44                        1.002632
PPGROUP_11                    1.136280

Removed AFTER this step: LOC_55 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8304  -0.7424  -0.1372   0.8191   3.2006  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    2.98489    0.18683  15.977  < 2e-16 ***
GSEGRD                        -0.56208    0.11306  -4.972 6.64e-07 ***
LowerLimitAge                 -1.99829    0.11716 -17.056  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      0.99147    0.08777  11.296  < 2e-16 ***
BLS_FEDERAL_Quits_Rate         0.13417    0.09348   1.435 0.151202    
BLS_FEDERAL_JobOpenings_Level  0.62073    0.06640   9.348  < 2e-16 ***
LOSSqrt                       -6.65252    0.16879 -39.414  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20409    0.10474  -1.949 0.051348 .  
AGELVL_B                      -0.56940    0.21791  -2.613 0.008975 ** 
AGELVL_C                      -0.17374    0.08502  -2.044 0.040989 *  
AGELVL_E                       0.28150    0.06711   4.195 2.73e-05 ***
AGELVL_F                       0.49837    0.06982   7.138 9.49e-13 ***
AGELVL_G                       0.72354    0.07370   9.817  < 2e-16 ***
AGELVL_H                       0.85673    0.07598  11.276  < 2e-16 ***
AGELVL_I                       0.72465    0.08095   8.952  < 2e-16 ***
LOC_01                        -0.24369    0.16402  -1.486 0.137351    
LOC_04                         0.48919    0.13198   3.706 0.000210 ***
LOC_06                         0.38523    0.08135   4.736 2.18e-06 ***
LOC_17                        -0.25335    0.14042  -1.804 0.071199 .  
LOC_20                         0.58177    0.25250   2.304 0.021220 *  
LOC_24                        -0.13803    0.07722  -1.788 0.073849 .  
LOC_29                        -0.38628    0.16485  -2.343 0.019120 *  
LOC_30                         0.64015    0.23655   2.706 0.006805 ** 
LOC_35                         0.43381    0.14628   2.966 0.003021 ** 
LOC_39                        -0.31924    0.13817  -2.310 0.020861 *  
LOC_42                        -0.32852    0.13940  -2.357 0.018436 *  
LOC_46                         0.77354    0.25022   3.091 0.001992 ** 
LOC_48                         0.21732    0.08634   2.517 0.011833 *  
LOC_51                        -0.18035    0.07205  -2.503 0.012311 *  
LOC_53                         0.36656    0.12347   2.969 0.002989 ** 
TOA_20                         1.10614    0.19185   5.766 8.14e-09 ***
TOA_30                         0.33529    0.08692   3.857 0.000115 ***
TOA_35                        -0.59717    0.28344  -2.107 0.035130 *  
TOA_38                         0.30290    0.06402   4.732 2.23e-06 ***
TOA_42                         1.13303    0.44047   2.572 0.010101 *  
TOA_44                         2.13910    1.07165   1.996 0.045925 *  
PPGROUP_11                    -0.42949    0.11592  -3.705 0.000211 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14564  on 14883  degrees of freedom
AIC: 14638

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.435977
LowerLimitAge                 2.229347
BLS_FEDERAL_OtherSep_Rate     1.338907
BLS_FEDERAL_Quits_Rate        1.208788
BLS_FEDERAL_JobOpenings_Level 1.087945
LOSSqrt                       1.411848
SEPCount_EFDATE_OCCLog        1.091253
AGELVL_B                      1.152168
AGELVL_C                      1.653359
AGELVL_E                      1.400710
AGELVL_F                      1.277035
AGELVL_G                      1.262333
AGELVL_H                      1.355247
AGELVL_I                      1.509236
LOC_01                        1.019713
LOC_04                        1.046827
LOC_06                        1.073693
LOC_17                        1.024316
LOC_20                        1.010429
LOC_24                        1.114786
LOC_29                        1.021359
LOC_30                        1.017101
LOC_35                        1.037548
LOC_39                        1.027798
LOC_42                        1.029238
LOC_46                        1.017318
LOC_48                        1.077540
LOC_51                        1.103901
LOC_53                        1.036661
TOA_20                        1.014267
TOA_30                        1.182683
TOA_35                        1.053172
TOA_38                        1.065754
TOA_42                        1.006473
TOA_44                        1.002628
PPGROUP_11                    1.136089

Removed AFTER this step: BLS_FEDERAL_Quits_Rate 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8339  -0.7430  -0.1386   0.8205   3.1825  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    3.03109    0.18406  16.468  < 2e-16 ***
GSEGRD                        -0.56328    0.11304  -4.983 6.26e-07 ***
LowerLimitAge                 -2.00191    0.11712 -17.092  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      1.04286    0.08021  13.001  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level  0.62838    0.06623   9.487  < 2e-16 ***
LOSSqrt                       -6.65564    0.16876 -39.439  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20189    0.10472  -1.928 0.053873 .  
AGELVL_B                      -0.56472    0.21782  -2.593 0.009527 ** 
AGELVL_C                      -0.17432    0.08500  -2.051 0.040287 *  
AGELVL_E                       0.28121    0.06710   4.191 2.78e-05 ***
AGELVL_F                       0.49791    0.06982   7.132 9.92e-13 ***
AGELVL_G                       0.72431    0.07369   9.829  < 2e-16 ***
AGELVL_H                       0.85661    0.07597  11.275  < 2e-16 ***
AGELVL_I                       0.72408    0.08094   8.946  < 2e-16 ***
LOC_01                        -0.24447    0.16401  -1.491 0.136061    
LOC_04                         0.49276    0.13193   3.735 0.000188 ***
LOC_06                         0.38600    0.08135   4.745 2.09e-06 ***
LOC_17                        -0.25254    0.14035  -1.799 0.071972 .  
LOC_20                         0.58653    0.25254   2.323 0.020205 *  
LOC_24                        -0.13781    0.07721  -1.785 0.074267 .  
LOC_29                        -0.38762    0.16485  -2.351 0.018705 *  
LOC_30                         0.63916    0.23678   2.699 0.006946 ** 
LOC_35                         0.43532    0.14630   2.976 0.002924 ** 
LOC_39                        -0.31761    0.13817  -2.299 0.021526 *  
LOC_42                        -0.32839    0.13944  -2.355 0.018520 *  
LOC_46                         0.77764    0.25048   3.105 0.001905 ** 
LOC_48                         0.21899    0.08632   2.537 0.011184 *  
LOC_51                        -0.18152    0.07204  -2.520 0.011742 *  
LOC_53                         0.36815    0.12347   2.982 0.002866 ** 
TOA_20                         1.10486    0.19176   5.762 8.33e-09 ***
TOA_30                         0.33680    0.08692   3.875 0.000107 ***
TOA_35                        -0.59559    0.28332  -2.102 0.035536 *  
TOA_38                         0.30517    0.06400   4.768 1.86e-06 ***
TOA_42                         1.13029    0.44033   2.567 0.010260 *  
TOA_44                         2.13675    1.07125   1.995 0.046084 *  
PPGROUP_11                    -0.42927    0.11591  -3.704 0.000213 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14566  on 14884  degrees of freedom
AIC: 14638

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.435633
LowerLimitAge                 2.228674
BLS_FEDERAL_OtherSep_Rate     1.117995
BLS_FEDERAL_JobOpenings_Level 1.081106
LOSSqrt                       1.411994
SEPCount_EFDATE_OCCLog        1.090958
AGELVL_B                      1.151897
AGELVL_C                      1.653567
AGELVL_E                      1.400770
AGELVL_F                      1.277023
AGELVL_G                      1.262300
AGELVL_H                      1.354998
AGELVL_I                      1.508727
LOC_01                        1.019717
LOC_04                        1.046466
LOC_06                        1.073594
LOC_17                        1.024308
LOC_20                        1.010226
LOC_24                        1.114790
LOC_29                        1.021318
LOC_30                        1.017084
LOC_35                        1.037466
LOC_39                        1.027712
LOC_42                        1.029216
LOC_46                        1.017137
LOC_48                        1.077366
LOC_51                        1.103727
LOC_53                        1.036590
TOA_20                        1.014292
TOA_30                        1.182450
TOA_35                        1.053153
TOA_38                        1.065029
TOA_42                        1.006464
TOA_44                        1.002625
PPGROUP_11                    1.136123

Removed AFTER this step: LOC_01 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8357  -0.7426  -0.1396   0.8210   3.1864  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    3.02648    0.18402  16.447  < 2e-16 ***
GSEGRD                        -0.56298    0.11305  -4.980 6.36e-07 ***
LowerLimitAge                 -2.00050    0.11711 -17.082  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      1.04209    0.08019  12.994  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level  0.62927    0.06623   9.502  < 2e-16 ***
LOSSqrt                       -6.66482    0.16867 -39.513  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20320    0.10472  -1.940 0.052331 .  
AGELVL_B                      -0.56643    0.21770  -2.602 0.009271 ** 
AGELVL_C                      -0.17570    0.08497  -2.068 0.038667 *  
AGELVL_E                       0.28258    0.06709   4.212 2.53e-05 ***
AGELVL_F                       0.49984    0.06981   7.160 8.09e-13 ***
AGELVL_G                       0.72484    0.07369   9.836  < 2e-16 ***
AGELVL_H                       0.85670    0.07597  11.277  < 2e-16 ***
AGELVL_I                       0.72370    0.08094   8.942  < 2e-16 ***
LOC_04                         0.50066    0.13185   3.797 0.000146 ***
LOC_06                         0.39418    0.08118   4.856 1.20e-06 ***
LOC_17                        -0.24441    0.14028  -1.742 0.081439 .  
LOC_20                         0.59471    0.25256   2.355 0.018538 *  
LOC_24                        -0.12996    0.07704  -1.687 0.091603 .  
LOC_29                        -0.37954    0.16479  -2.303 0.021270 *  
LOC_30                         0.64741    0.23680   2.734 0.006256 ** 
LOC_35                         0.44354    0.14623   3.033 0.002419 ** 
LOC_39                        -0.30923    0.13808  -2.239 0.025127 *  
LOC_42                        -0.32005    0.13935  -2.297 0.021634 *  
LOC_46                         0.78576    0.25049   3.137 0.001707 ** 
LOC_48                         0.22702    0.08617   2.634 0.008429 ** 
LOC_51                        -0.17368    0.07185  -2.417 0.015647 *  
LOC_53                         0.37644    0.12336   3.052 0.002277 ** 
TOA_20                         1.10735    0.19165   5.778 7.56e-09 ***
TOA_30                         0.33979    0.08690   3.910 9.22e-05 ***
TOA_35                        -0.59270    0.28330  -2.092 0.036427 *  
TOA_38                         0.30414    0.06400   4.752 2.01e-06 ***
TOA_42                         1.12704    0.44028   2.560 0.010473 *  
TOA_44                         2.14399    1.07124   2.001 0.045347 *  
PPGROUP_11                    -0.43015    0.11591  -3.711 0.000206 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14568  on 14885  degrees of freedom
AIC: 14638

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.435790
LowerLimitAge                 2.228846
BLS_FEDERAL_OtherSep_Rate     1.118045
BLS_FEDERAL_JobOpenings_Level 1.080979
LOSSqrt                       1.411164
SEPCount_EFDATE_OCCLog        1.091001
AGELVL_B                      1.152035
AGELVL_C                      1.653633
AGELVL_E                      1.400446
AGELVL_F                      1.276537
AGELVL_G                      1.262359
AGELVL_H                      1.355196
AGELVL_I                      1.509271
LOC_04                        1.044765
LOC_06                        1.068700
LOC_17                        1.022750
LOC_20                        1.009747
LOC_24                        1.109539
LOC_29                        1.020207
LOC_30                        1.016531
LOC_35                        1.035993
LOC_39                        1.026006
LOC_42                        1.027542
LOC_46                        1.016655
LOC_48                        1.073172
LOC_51                        1.097751
LOC_53                        1.034479
TOA_20                        1.014194
TOA_30                        1.181889
TOA_35                        1.053121
TOA_38                        1.064879
TOA_42                        1.006467
TOA_44                        1.002604
PPGROUP_11                    1.135982

Removed AFTER this step: LOC_24 



Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8398  -0.7438  -0.1399   0.8201   3.1933  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    3.00257    0.18338  16.373  < 2e-16 ***
GSEGRD                        -0.58663    0.11220  -5.229 1.71e-07 ***
LowerLimitAge                 -1.99745    0.11707 -17.062  < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate      1.04364    0.08018  13.016  < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level  0.63022    0.06622   9.518  < 2e-16 ***
LOSSqrt                       -6.65385    0.16850 -39.490  < 2e-16 ***
SEPCount_EFDATE_OCCLog        -0.20105    0.10470  -1.920 0.054827 .  
AGELVL_B                      -0.56801    0.21779  -2.608 0.009107 ** 
AGELVL_C                      -0.17721    0.08497  -2.086 0.037012 *  
AGELVL_E                       0.28327    0.06709   4.222 2.42e-05 ***
AGELVL_F                       0.49911    0.06982   7.149 8.75e-13 ***
AGELVL_G                       0.72496    0.07367   9.841  < 2e-16 ***
AGELVL_H                       0.85670    0.07594  11.281  < 2e-16 ***
AGELVL_I                       0.72212    0.08090   8.926  < 2e-16 ***
LOC_04                         0.51863    0.13141   3.947 7.93e-05 ***
LOC_06                         0.41164    0.08052   5.112 3.18e-07 ***
LOC_17                        -0.22648    0.13989  -1.619 0.105455    
LOC_20                         0.61205    0.25234   2.425 0.015288 *  
LOC_29                        -0.36318    0.16454  -2.207 0.027295 *  
LOC_30                         0.66365    0.23658   2.805 0.005029 ** 
LOC_35                         0.46107    0.14584   3.161 0.001570 ** 
LOC_39                        -0.29150    0.13769  -2.117 0.034252 *  
LOC_42                        -0.30190    0.13893  -2.173 0.029777 *  
LOC_46                         0.80096    0.25034   3.199 0.001377 ** 
LOC_48                         0.24549    0.08547   2.872 0.004075 ** 
LOC_51                        -0.15381    0.07089  -2.170 0.030027 *  
LOC_53                         0.39453    0.12287   3.211 0.001324 ** 
TOA_20                         1.11124    0.19157   5.801 6.61e-09 ***
TOA_30                         0.35415    0.08650   4.094 4.24e-05 ***
TOA_35                        -0.60626    0.28298  -2.142 0.032163 *  
TOA_38                         0.31275    0.06378   4.904 9.41e-07 ***
TOA_42                         1.12464    0.44033   2.554 0.010647 *  
TOA_44                         2.17296    1.07108   2.029 0.042483 *  
PPGROUP_11                    -0.41882    0.11559  -3.623 0.000291 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 20683  on 14919  degrees of freedom
Residual deviance: 14571  on 14886  degrees of freedom
AIC: 14639

Number of Fisher Scoring iterations: 5

                                   VIF
GSEGRD                        1.414132
LowerLimitAge                 2.228218
BLS_FEDERAL_OtherSep_Rate     1.118058
BLS_FEDERAL_JobOpenings_Level 1.081055
LOSSqrt                       1.408028
SEPCount_EFDATE_OCCLog        1.090862
AGELVL_B                      1.151972
AGELVL_C                      1.653603
AGELVL_E                      1.400482
AGELVL_F                      1.276403
AGELVL_G                      1.262428
AGELVL_H                      1.355066
AGELVL_I                      1.508723
LOC_04                        1.037962
LOC_06                        1.051265
LOC_17                        1.016828
LOC_20                        1.008066
LOC_29                        1.016635
LOC_30                        1.014882
LOC_35                        1.030761
LOC_39                        1.020027
LOC_42                        1.021352
LOC_46                        1.015328
LOC_48                        1.055825
LOC_51                        1.067927
LOC_53                        1.026646
TOA_20                        1.014077
TOA_30                        1.170882
TOA_35                        1.052610
TOA_38                        1.057914
TOA_42                        1.006489
TOA_44                        1.002348
PPGROUP_11                    1.132533

Removed AFTER this step: LOC_17 



Following variables removed based on p-values (in order of removal):
 [1] "TOA_45"                   "LOC_21"                  
 [3] "LOC_09"                   "LOC_36"                  
 [5] "LOC_54"                   "LOC_16"                  
 [7] "LOC_23"                   "LOC_45"                  
 [9] "LOC_12"                   "SalaryOverUnderIndAvg"   
[11] "LOC_05"                   "LOC_10"                  
[13] "BLS_FEDERAL_Layoffs_Rate" "LOC_31"                  
[15] "LOC_33"                   "LOC_37"                  
[17] "LOC_26"                   "LOC_15"                  
[19] "LOC_41"                   "TOA_32"                  
[21] "LOC_28"                   "LOC_22"                  
[23] "TOA_40"                   "LOC_02"                  
[25] "LOC_19"                   "LOC_44"                  
[27] "LOC_50"                   "LOC_25"                  
[29] "LOC_32"                   "LOC_40"                  
[31] "LOC_08"                   "TOA_15"                  
[33] "LOC_27"                   "LOC_18"                  
[35] "LOC_47"                   "LOC_49"                  
[37] "LOC_34"                   "LOC_38"                  
[39] "LOC_13"                   "LOC_55"                  
[41] "BLS_FEDERAL_Quits_Rate"   "LOC_01"                  
[43] "LOC_24"                   "LOC_17"                  


Null Deviances (in order):
 [1] "6140.09618413702" "6139.70530359788" "6139.68430874922" "6139.65854475106"
 [5] "6139.63313569129" "6139.59532196834" "6139.53772461692" "6139.48366441678"
 [9] "6139.39728155705" "6139.29830098624" "6139.1900882605"  "6139.08688894165"
[13] "6138.98092947028" "6138.85674008882" "6138.72579420097" "6138.59742406333"
[17] "6138.47085903648" "6138.27554988296" "6138.0311195164"  "6137.73506640671"
[21] "6137.4303765745"  "6137.10477253365" "6136.75626463363" "6136.40051668325"
[25] "6136.01092268138" "6135.58921284231" "6135.16054261118" "6134.63328743168"
[29] "6134.04181569797" "6133.25422902175" "6132.31543711746" "6131.41750130868"
[33] "6130.31513080467" "6129.17306670617" "6127.97760516643" "6126.80371644396"
[37] "6125.66955201923" "6124.55307944912" "6122.89611809423" "6121.27266108516"
[41] "6119.17124816164" "6117.1107132246"  "6114.87372224233" "6112.02966681198"

Min value at iteration =  44

Diff Degrees of Freedom (in order):
 [1] "76" "75" "74" "73" "72" "71" "70" "69" "68" "67" "66" "65" "64" "63" "62"
[16] "61" "60" "59" "58" "57" "56" "55" "54" "53" "52" "51" "50" "49" "48" "47"
[31] "46" "45" "44" "43" "42" "41" "40" "39" "38" "37" "36" "35" "34" "33"

Min value at iteration =  44

Log Likelihoods (in order):
 [1] "-7271.52432906914" "-7271.71976933871" "-7271.73026676304"
 [4] "-7271.74314876212" "-7271.75585329201" "-7271.77476015348"
 [7] "-7271.80355882919" "-7271.83058892926" "-7271.87378035912"
[10] "-7271.92327064453" "-7271.9773770074"  "-7272.02897666682"
[13] "-7272.08195640251" "-7272.14405109324" "-7272.20952403716"
[16] "-7272.27370910599" "-7272.33699161941" "-7272.43464619617"
[19] "-7272.55686137945" "-7272.70488793429" "-7272.8572328504" 
[22] "-7273.02003487083" "-7273.19428882084" "-7273.37216279602"
[25] "-7273.56695979696" "-7273.7778147165"  "-7273.99214983206"
[28] "-7274.25577742181" "-7274.55151328866" "-7274.94530662677"
[31] "-7275.41470257892" "-7275.86367048331" "-7276.41485573531"
[34] "-7276.98588778457" "-7277.58361855443" "-7278.17056291567"
[37] "-7278.73764512804" "-7279.29588141309" "-7280.12436209054"
[40] "-7280.93609059507" "-7281.98679705683" "-7283.01706452535"
[43] "-7284.13556001649" "-7285.55758773166"

Min value at iteration =  44

AIC values (in order):
 [1] "14697.0486581383" "14695.4395386774" "14693.4605335261" "14691.4862975242"
 [5] "14689.511706584"  "14687.549520307"  "14685.6071176584" "14683.6611778585"
 [9] "14681.7475607182" "14679.8465412891" "14677.9547540148" "14676.0579533336"
[13] "14674.163912805"  "14672.2881021865" "14670.4190480743" "14668.547418212" 
[17] "14666.6739832388" "14664.8692923923" "14663.1137227589" "14661.4097758686"
[21] "14659.7144657008" "14658.0400697417" "14656.3885776417" "14654.744325592" 
[25] "14653.1339195939" "14651.555629433"  "14649.9842996641" "14648.5115548436"
[29] "14647.1030265773" "14645.8906132535" "14644.8294051578" "14643.7273409666"
[33] "14642.8297114706" "14641.9717755691" "14641.1672371089" "14640.3411258313"
[37] "14639.4752902561" "14638.5917628262" "14638.2487241811" "14637.8721811901"
[41] "14637.9735941137" "14638.0341290507" "14638.271120033"  "14639.1151754633"

Min value at iteration =  40

BIC values (in order):
 [1] "15283.0539144176" "15273.834337083"  "15264.2448740579" "15254.6601801823"
 [5] "15245.0751313683" "15235.5024872175" "15225.9496266952" "15216.3932290216"
 [9] "15206.8691540075" "15197.3576767046" "15187.8554315566" "15178.3481730017"
[13] "15168.8436745993" "15159.357406107"  "15149.8778941211" "15140.395806385" 
[17] "15130.911913538"  "15121.4967648178" "15112.1307373106" "15102.8163325465"
[21] "15093.510564505"  "15084.2257106721" "15074.9637606983" "15065.709050775" 
[25] "15056.4881869031" "15047.2994388684" "15038.1176512258" "15029.0344485315"
[29] "15020.0154623915" "15011.1925911939" "15002.5209252245" "14993.8084031595"
[33] "14985.3003157897" "14976.8319220145" "14968.4169256805" "14959.9803565292"
[37] "14951.5040630801" "14943.0100777765" "14935.0565812576" "14927.0695803929"
[41] "14919.5605354427" "14912.010612506"  "14904.6371456145" "14897.8707431711"

Min value at iteration =  44
In [141]:
%R LRSigCols <- varsP.Repeat
Out[141]:
array(['GSEGRD', 'LowerLimitAge', 'BLS_FEDERAL_OtherSep_Rate',
       'BLS_FEDERAL_JobOpenings_Level', 'LOSSqrt',
       'SEPCount_EFDATE_OCCLog', 'AGELVL_B', 'AGELVL_C', 'AGELVL_E',
       'AGELVL_F', 'AGELVL_G', 'AGELVL_H', 'AGELVL_I', 'LOC_04', 'LOC_06',
       'LOC_20', 'LOC_29', 'LOC_30', 'LOC_35', 'LOC_39', 'LOC_42',
       'LOC_46', 'LOC_48', 'LOC_51', 'LOC_53', 'TOA_20', 'TOA_30',
       'TOA_35', 'TOA_38', 'TOA_42', 'TOA_44', 'PPGROUP_11'], 
      dtype='<U29')
In [142]:
%R -o LRSigCols
In [143]:
#from rpy2.robjects import pandas2ri
#pandas2ri.activate()

#LRSigCols
LRSigCols = pandas2ri.ri2py(LRSigCols)
LRSigCols = LRSigCols.tolist()

Only following variables remain after Logistic Regression manual selection:

In [144]:
LRSigCols
Out[144]:
['GSEGRD',
 'LowerLimitAge',
 'BLS_FEDERAL_OtherSep_Rate',
 'BLS_FEDERAL_JobOpenings_Level',
 'LOSSqrt',
 'SEPCount_EFDATE_OCCLog',
 'AGELVL_B',
 'AGELVL_C',
 'AGELVL_E',
 'AGELVL_F',
 'AGELVL_G',
 'AGELVL_H',
 'AGELVL_I',
 'LOC_04',
 'LOC_06',
 'LOC_20',
 'LOC_29',
 'LOC_30',
 'LOC_35',
 'LOC_39',
 'LOC_42',
 'LOC_46',
 'LOC_48',
 'LOC_51',
 'LOC_53',
 'TOA_20',
 'TOA_30',
 'TOA_35',
 'TOA_38',
 'TOA_42',
 'TOA_44',
 'PPGROUP_11']
In [145]:
%%time

def lr_explorBinary(  cost,
                      Data        = OPMAnalysisDataNoFamBinary,
                      cols        = PCList,
                      cv          = cv,
                      seed        = seed):
    
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    if ("SEP" in cols):    X = Data[cols].drop("SEP", axis=1).as_matrix() 
    else: X = Data[cols]
    
    lr_clf = LogisticRegression(penalty='l2', C=cost, class_weight=None, random_state=seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler',MinMaxScaler()),
         ('CLF',lr_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    return accuracy
CPU times: user 6 µs, sys: 0 ns, total: 6 µs
Wall time: 12.4 µs
In [146]:
%%time

def lr_explorBinary_w_PCA(cost,
                          PCA,
                          Data        = OPMAnalysisDataNoFamBinary,
                          cv          = cv,
                          seed        = seed):
    
    startTime = datetime.now()
    y = Data["SEP"].values # get the labels we want    
    
    X = Data.drop("SEP", axis=1).as_matrix() 
    
    lr_clf = LogisticRegression(penalty='l2', C=cost, class_weight=None, random_state=seed) # get object
    
    # setup pipeline to take PCA, then fit a clf model
    clf_pipe = Pipeline(
        [('minMaxScaler',MinMaxScaler()),
         ('PCA',PCA),
         ('CLF',lr_clf)]
    )

    accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
    MeanAccuracy =  sum(accuracy)/len(accuracy)
    accuracy = np.append(accuracy, MeanAccuracy)
    endTime = datetime.now()
    TotalTime = endTime - startTime
    accuracy = np.append(accuracy, TotalTime)
    return accuracy
CPU times: user 5 µs, sys: 1 µs, total: 6 µs
Wall time: 9.3 µs
In [147]:
%%time

##Full Columns
acclist = [] 

cost    = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]

for i in range(0,len(cost)):
    acclist.append(lr_explorBinary(cost       = cost[i],
                             cols       = fullColumns))

LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "Logistic Regression: All Raw Features",
                                                "Cost": cost
                                              })[["ModelVersion", "Cost"]],
                               pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist

##Reduced Columns
acclist = [] 

cost    = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]

for i in range(0,len(cost)):
    acclist.append(lr_explorBinary(cost       = cost[i]))

LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "Logistic Regression: Top 15 from PCA Raw Features",
                                                "Cost": cost
                                              })[["ModelVersion", "Cost"]],
                               pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist

##With PCA
acclist = [] 

cost    = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]

for i in range(0,len(cost)):
    acclist.append(lr_explorBinary_w_PCA(cost       = cost[i],
                                   PCA        = PCA(n_components=23, svd_solver='randomized', random_state = seed)))

LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "Logistic Regression: With PCA",
                                                "Cost": cost
                                              })[["ModelVersion", "Cost"]],
                               pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist


##Significant Column List from Manual Tuning in R
acclist = [] 

cost    = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]

for i in range(0,len(cost)):
    acclist.append(lr_explorBinary(cost       = cost[i],
                             cols       = LRSigCols))

LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "Logistic Regression: Manual Significant Features",
                                                "Cost": cost
                                              })[["ModelVersion", "Cost"]],
                               pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist
ModelVersion Cost Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Logistic Regression: All Raw Features 1.000000e-08 0.591290 0.676717 0.593499 0.619511 0.621857 0.620575 00:00:00.319026
1 Logistic Regression: All Raw Features 1.000000e-04 0.649246 0.582580 0.693700 0.698961 0.715052 0.667908 00:00:00.429256
2 Logistic Regression: All Raw Features 1.000000e-03 0.667002 0.565159 0.716823 0.725109 0.722762 0.679371 00:00:00.451890
3 Logistic Regression: All Raw Features 1.000000e-02 0.654271 0.562814 0.714477 0.741200 0.728797 0.680312 00:00:00.441498
4 Logistic Regression: All Raw Features 5.000000e-02 0.641541 0.572194 0.721180 0.754274 0.731814 0.684201 00:00:00.563521
5 Logistic Regression: All Raw Features 1.000000e+00 0.644556 0.672027 0.719169 0.749581 0.670466 0.691160 00:00:00.837317
6 Logistic Regression: All Raw Features 2.000000e+00 0.635176 0.730988 0.726206 0.748240 0.658062 0.699735 00:00:00.947721
7 Logistic Regression: All Raw Features 3.000000e+00 0.622446 0.763484 0.730563 0.750922 0.651693 0.703821 00:00:01.047439
8 Logistic Regression: All Raw Features 4.000000e+00 0.619095 0.781240 0.733914 0.752263 0.647335 0.706769 00:00:01.063703
9 Logistic Regression: All Raw Features 5.000000e+00 0.611725 0.790955 0.734584 0.755615 0.647000 0.707976 00:00:01.071100
ModelVersion Cost Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Logistic Regression: Top 15 from PCA Raw Features 1.000000e-08 0.597320 0.678727 0.597855 0.624204 0.625880 0.624797 00:00:00.336265
1 Logistic Regression: Top 15 from PCA Raw Features 1.000000e-04 0.650921 0.582915 0.694705 0.699631 0.714381 0.668511 00:00:00.371418
2 Logistic Regression: Top 15 from PCA Raw Features 1.000000e-03 0.665327 0.564154 0.716488 0.725779 0.722762 0.678902 00:00:00.412857
3 Logistic Regression: Top 15 from PCA Raw Features 1.000000e-02 0.651591 0.563149 0.714812 0.742876 0.729802 0.680446 00:00:00.470093
4 Logistic Regression: Top 15 from PCA Raw Features 5.000000e-02 0.638861 0.569514 0.720509 0.752933 0.735501 0.683464 00:00:00.544142
5 Logistic Regression: Top 15 from PCA Raw Features 1.000000e+00 0.642881 0.681742 0.725201 0.750587 0.669460 0.693974 00:00:00.754557
6 Logistic Regression: Top 15 from PCA Raw Features 2.000000e+00 0.635846 0.744054 0.731568 0.751257 0.656051 0.703755 00:00:00.855511
7 Logistic Regression: Top 15 from PCA Raw Features 3.000000e+00 0.625796 0.781575 0.735590 0.752598 0.651022 0.709316 00:00:00.933511
8 Logistic Regression: Top 15 from PCA Raw Features 4.000000e+00 0.617755 0.793970 0.740282 0.755280 0.646664 0.710790 00:00:00.903998
9 Logistic Regression: Top 15 from PCA Raw Features 5.000000e+00 0.612060 0.796650 0.740617 0.755615 0.645323 0.710053 00:00:00.998530
ModelVersion Cost Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Logistic Regression: With PCA 1.000000e-08 0.694472 0.637521 0.712466 0.728797 0.716058 0.697863 00:00:00.812918
1 Logistic Regression: With PCA 1.000000e-04 0.691457 0.617755 0.709786 0.729132 0.716728 0.692972 00:00:00.821289
2 Logistic Regression: With PCA 1.000000e-03 0.682077 0.567504 0.710791 0.733155 0.716058 0.681917 00:00:00.947737
3 Logistic Regression: With PCA 1.000000e-02 0.642211 0.557789 0.700737 0.731814 0.707342 0.667978 00:00:01.012688
4 Logistic Regression: With PCA 5.000000e-02 0.626466 0.554104 0.692694 0.735501 0.711700 0.664093 00:00:00.984053
5 Logistic Regression: With PCA 1.000000e+00 0.617755 0.551424 0.688673 0.736172 0.711029 0.661011 00:00:01.064958
6 Logistic Regression: With PCA 2.000000e+00 0.617085 0.551089 0.688673 0.736507 0.710359 0.660743 00:00:00.971489
7 Logistic Regression: With PCA 3.000000e+00 0.616750 0.551089 0.689008 0.736507 0.710023 0.660676 00:00:01.011076
8 Logistic Regression: With PCA 4.000000e+00 0.617085 0.551089 0.689008 0.736507 0.710359 0.660810 00:00:00.977702
9 Logistic Regression: With PCA 5.000000e+00 0.617085 0.551089 0.688673 0.736507 0.710359 0.660743 00:00:01.061035
ModelVersion Cost Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Logistic Regression: Manual Significant Features 1.000000e-08 0.519933 0.548409 0.518432 0.522293 0.511901 0.524193 00:00:00.283672
1 Logistic Regression: Manual Significant Features 1.000000e-04 0.602010 0.649581 0.630697 0.621522 0.594033 0.619569 00:00:00.316684
2 Logistic Regression: Manual Significant Features 1.000000e-03 0.658291 0.738358 0.706099 0.706001 0.692591 0.700268 00:00:00.342519
3 Logistic Regression: Manual Significant Features 1.000000e-02 0.674372 0.781910 0.713472 0.738854 0.725444 0.726810 00:00:00.348444
4 Logistic Regression: Manual Significant Features 5.000000e-02 0.676717 0.797990 0.722185 0.754609 0.741871 0.738674 00:00:00.389914
5 Logistic Regression: Manual Significant Features 1.000000e+00 0.685427 0.802345 0.732574 0.756956 0.746564 0.744773 00:00:00.495491
6 Logistic Regression: Manual Significant Features 2.000000e+00 0.685427 0.801675 0.732909 0.756956 0.747234 0.744840 00:00:00.508891
7 Logistic Regression: Manual Significant Features 3.000000e+00 0.686767 0.801340 0.732574 0.755280 0.747234 0.744639 00:00:00.514269
8 Logistic Regression: Manual Significant Features 4.000000e+00 0.687102 0.801340 0.732574 0.754274 0.747234 0.744505 00:00:00.519364
9 Logistic Regression: Manual Significant Features 5.000000e+00 0.687102 0.802010 0.732574 0.754274 0.747234 0.744639 00:00:00.516074
CPU times: user 2min 35s, sys: 14min 29s, total: 17min 5s
Wall time: 27.8 s
In [148]:
display(TopResultsDF)

plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))

FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
ModelVersion Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy
0 Logistic Regression: Manual Significant Features 0.685427 0.801675 0.732909 0.756956 0.747234 0.744840
1 Logistic Regression: Top 15 from PCA Raw Features 0.617755 0.793970 0.740282 0.755280 0.646664 0.710790
2 Logistic Regression: All Raw Features 0.611725 0.790955 0.734584 0.755615 0.647000 0.707976
3 Logistic Regression: With PCA 0.694472 0.637521 0.712466 0.728797 0.716058 0.697863
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [149]:
lr_clf = LogisticRegression(penalty='l2', C=2, class_weight=None, random_state=seed) # get object

lr_clf, lr_acc = compute_kfold_scores_ClassificationBinary(clf         = lr_clf,
                                                           cols        = LRSigCols)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          988   512  1500
SC          427  1058  1485
All        1415  1570  2985

Normalized confusion matrix
[[ 0.65866667  0.34133333]
 [ 0.28754209  0.71245791]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1206   294  1500
SC          298  1187  1485
All        1504  1481  2985

Normalized confusion matrix
[[ 0.804      0.196    ]
 [ 0.2006734  0.7993266]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted   NS    SC   All
True                      
NS         817   682  1499
SC         115  1370  1485
All        932  2052  2984

Normalized confusion matrix
[[ 0.54503002  0.45496998]
 [ 0.07744108  0.92255892]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1050   449  1499
SC          276  1208  1484
All        1326  1657  2983

Normalized confusion matrix
[[ 0.70046698  0.29953302]
 [ 0.18598383  0.81401617]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1052   447  1499
SC          307  1177  1484
All        1359  1624  2983

Normalized confusion matrix
[[ 0.7018012   0.2981988 ]
 [ 0.20687332  0.79312668]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.68543, 0.80168, 0.73291, 0.75696, 0.74723]

        Average Accuracy: 0.74484

Retest Random Forest and KNN with Logistic Regression Significant Columns

In [150]:
#### Random Forest Sign. Cols

acclist = []

n_estimators       =  [10    , 10     , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10   , 10   , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 10    , 15    , 20    , 30    , 50    ]  
max_features       =  ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 5     , 10    , 15   , 20   , None  , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     , 5     ] 
max_depth          =  [None  , None   , None  , None  , None  , None  , None  , None  , None  , None , None , None  , 10    , 15    , 20    , 25    , 30    , 3     , 4     , 5     , 6     , 7     , 8     , 9     , 11    , 12    , 13    , 4     , 4     , 4     , 4     ] 
min_samples_split  =  [2     , 8      , 12    , 18    , 20    , 24    , 36    , 18    , 18    , 18   , 18   , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    , 18    ] 
min_samples_leaf   =  [1     , 4      , 6     , 9     , 10    , 12    , 18    , 9     , 9     , 9    , 9    , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     , 9     ]


## Model with only top 15 raw Scaled Principal Features 
for i in range(0,len(n_estimators)):
    acclist.append(rfc_explorBinary(n_estimators      = n_estimators[i],
                              max_features      = max_features[i],
                              max_depth         = max_depth[i],
                              min_samples_split = min_samples_split[i],
                              min_samples_leaf  = min_samples_leaf[i],
                              cols = LRSigCols
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({  "ModelVersion": "Random Forest: With LR Sig Cols",
                                                "n_estimators": n_estimators,          
                                                "max_features": max_features,         
                                                "max_depth": max_depth,        
                                                "min_samples_split": min_samples_split,
                                                "min_samples_leaf": min_samples_leaf   
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist


#### KNN Sign. Cols

acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 200        , 200        , 200        , 200        , 200        , 200        , 200     , 200]
algorithm   =  'ball_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5         , 20        , 50          , 100      , 150]



for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              cols = LRSigCols
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", With LR Sig Cols",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist


acclist = [] 

n_neighbors =  [5          , 10         , 15         , 20         , 30         , 40         , 50         , 100        , 150        , 200        , 250        , 200        , 200        , 200        , 200        , 200        , 200        , 200     , 200]
algorithm   =  'kd_tree'
leaf_size   =  [30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 30         , 2          , 3          , 4          , 5         , 20        , 50          , 100      , 150]



for i in range(0,len(n_neighbors)):
    acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
                              algorithm   = algorithm,
                              leaf_size   = leaf_size[i],
                              cols = LRSigCols
                             )
                  )

rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
                                                "ModelVersion": "KNN: " + algorithm + ", With LR Sig Cols",
                                                "n_neighbors": n_neighbors,          
                                                "algorithm": algorithm,         
                                                "leaf_size": leaf_size  
                                              }),
                               pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
ModelVersion max_depth max_features min_samples_leaf min_samples_split n_estimators Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 Random Forest: With LR Sig Cols NaN auto 1 2 10 0.591625 0.705193 0.717493 0.729467 0.674154 0.683586 00:00:01.311026
1 Random Forest: With LR Sig Cols NaN auto 4 8 10 0.649916 0.733668 0.712131 0.747234 0.707006 0.709991 00:00:01.349356
2 Random Forest: With LR Sig Cols NaN auto 6 12 10 0.663987 0.726968 0.721180 0.739189 0.704995 0.711264 00:00:01.345558
3 Random Forest: With LR Sig Cols NaN auto 9 18 10 0.671357 0.722278 0.721180 0.753269 0.716058 0.716828 00:00:01.346600
4 Random Forest: With LR Sig Cols NaN auto 10 20 10 0.662647 0.731993 0.721180 0.743212 0.716393 0.715085 00:00:01.347007
5 Random Forest: With LR Sig Cols NaN auto 12 24 10 0.673367 0.747404 0.722185 0.747570 0.716393 0.721384 00:00:01.348220
6 Random Forest: With LR Sig Cols NaN auto 18 36 10 0.694472 0.736013 0.721180 0.749581 0.712705 0.722790 00:00:01.528591
7 Random Forest: With LR Sig Cols NaN 5 9 18 10 0.671357 0.722278 0.721180 0.753269 0.716058 0.716828 00:00:01.346245
8 Random Forest: With LR Sig Cols NaN 10 9 18 10 0.637856 0.712563 0.723525 0.746899 0.700637 0.704296 00:00:01.346380
9 Random Forest: With LR Sig Cols NaN 15 9 18 10 0.600000 0.715578 0.717493 0.732819 0.691586 0.691495 00:00:01.346615
10 Random Forest: With LR Sig Cols NaN 20 9 18 10 0.624121 0.710553 0.715818 0.736842 0.685551 0.694577 00:00:01.346949
11 Random Forest: With LR Sig Cols NaN None 9 18 10 0.609380 0.701508 0.717828 0.738183 0.683875 0.690155 00:00:01.347226
12 Random Forest: With LR Sig Cols 10.0 5 9 18 10 0.689782 0.736013 0.718164 0.750922 0.715052 0.721987 00:00:01.345113
13 Random Forest: With LR Sig Cols 15.0 5 9 18 10 0.680067 0.731658 0.726877 0.744552 0.717063 0.720044 00:00:01.346495
14 Random Forest: With LR Sig Cols 20.0 5 9 18 10 0.690787 0.725293 0.713807 0.752933 0.715387 0.719642 00:00:01.344865
15 Random Forest: With LR Sig Cols 25.0 5 9 18 10 0.671357 0.723953 0.721180 0.748910 0.708347 0.714749 00:00:01.346614
16 Random Forest: With LR Sig Cols 30.0 5 9 18 10 0.671357 0.722278 0.721180 0.753269 0.716058 0.716828 00:00:01.345887
17 Random Forest: With LR Sig Cols 3.0 5 9 18 10 0.694807 0.747739 0.729558 0.743882 0.685216 0.720240 00:00:01.347231
18 Random Forest: With LR Sig Cols 4.0 5 9 18 10 0.705193 0.754439 0.717158 0.746229 0.715722 0.727748 00:00:01.346079
19 Random Forest: With LR Sig Cols 5.0 5 9 18 10 0.696817 0.748744 0.725536 0.740865 0.722092 0.726811 00:00:01.346039
20 Random Forest: With LR Sig Cols 6.0 5 9 18 10 0.713233 0.722278 0.722855 0.752933 0.715722 0.725404 00:00:01.348854
21 Random Forest: With LR Sig Cols 7.0 5 9 18 10 0.705193 0.737018 0.715147 0.749581 0.728126 0.727013 00:00:01.346236
22 Random Forest: With LR Sig Cols 8.0 5 9 18 10 0.694137 0.719263 0.712131 0.742541 0.716393 0.716893 00:00:01.344935
23 Random Forest: With LR Sig Cols 9.0 5 9 18 10 0.686097 0.719598 0.722855 0.751592 0.727456 0.721520 00:00:01.347869
24 Random Forest: With LR Sig Cols 11.0 5 9 18 10 0.679397 0.728978 0.725871 0.745558 0.716728 0.719307 00:00:01.345858
25 Random Forest: With LR Sig Cols 12.0 5 9 18 10 0.688442 0.727638 0.723525 0.743212 0.710694 0.718702 00:00:01.345371
26 Random Forest: With LR Sig Cols 13.0 5 9 18 10 0.675377 0.733333 0.720174 0.755615 0.717063 0.720313 00:00:01.344974
27 Random Forest: With LR Sig Cols 4.0 5 9 18 15 0.716248 0.750419 0.720845 0.745893 0.715722 0.729825 00:00:01.379571
28 Random Forest: With LR Sig Cols 4.0 5 9 18 20 0.716918 0.756784 0.724531 0.748575 0.716728 0.732707 00:00:01.415835
29 Random Forest: With LR Sig Cols 4.0 5 9 18 30 0.714908 0.739698 0.729893 0.751928 0.724438 0.732173 00:00:01.475831
30 Random Forest: With LR Sig Cols 4.0 5 9 18 50 0.714908 0.740704 0.728217 0.749581 0.725109 0.731704 00:00:01.594223
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: ball_tree, With LR Sig Cols ball_tree 30 5 0.604690 0.721273 0.680630 0.690915 0.640965 0.667695 00:00:09.033146
1 KNN: ball_tree, With LR Sig Cols ball_tree 30 10 0.595645 0.709548 0.699397 0.708683 0.635267 0.669708 00:00:09.339628
2 KNN: ball_tree, With LR Sig Cols ball_tree 30 15 0.604355 0.726968 0.682976 0.718069 0.644653 0.675404 00:00:09.427573
3 KNN: ball_tree, With LR Sig Cols ball_tree 30 20 0.612395 0.721943 0.686327 0.715052 0.638619 0.674867 00:00:09.632660
4 KNN: ball_tree, With LR Sig Cols ball_tree 30 30 0.624791 0.726968 0.683311 0.719745 0.652028 0.681369 00:00:09.920288
5 KNN: ball_tree, With LR Sig Cols ball_tree 30 40 0.633501 0.723283 0.685657 0.723098 0.659068 0.684921 00:00:09.760009
6 KNN: ball_tree, With LR Sig Cols ball_tree 30 50 0.629816 0.720268 0.689008 0.720751 0.665102 0.684989 00:00:09.906413
7 KNN: ball_tree, With LR Sig Cols ball_tree 30 100 0.644891 0.699162 0.704088 0.724103 0.685887 0.691626 00:00:09.877033
8 KNN: ball_tree, With LR Sig Cols ball_tree 30 150 0.643216 0.694807 0.710791 0.727791 0.696614 0.694644 00:00:10.250216
9 KNN: ball_tree, With LR Sig Cols ball_tree 30 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:10.166679
10 KNN: ball_tree, With LR Sig Cols ball_tree 30 250 0.667002 0.704188 0.701072 0.704995 0.685551 0.692562 00:00:10.220543
11 KNN: ball_tree, With LR Sig Cols ball_tree 2 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:19.321254
12 KNN: ball_tree, With LR Sig Cols ball_tree 3 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:15.362882
13 KNN: ball_tree, With LR Sig Cols ball_tree 4 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:15.275130
14 KNN: ball_tree, With LR Sig Cols ball_tree 5 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:15.415284
15 KNN: ball_tree, With LR Sig Cols ball_tree 20 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:11.215757
16 KNN: ball_tree, With LR Sig Cols ball_tree 50 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:09.556898
17 KNN: ball_tree, With LR Sig Cols ball_tree 100 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:09.167441
18 KNN: ball_tree, With LR Sig Cols ball_tree 150 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:09.245038
ModelVersion algorithm leaf_size n_neighbors Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy RunTime
0 KNN: kd_tree, With LR Sig Cols kd_tree 30 5 0.604690 0.721943 0.680965 0.690915 0.640965 0.667896 00:00:03.055713
1 KNN: kd_tree, With LR Sig Cols kd_tree 30 10 0.595645 0.709548 0.699397 0.708683 0.635267 0.669708 00:00:03.592091
2 KNN: kd_tree, With LR Sig Cols kd_tree 30 15 0.604355 0.726968 0.682976 0.718069 0.644653 0.675404 00:00:03.754054
3 KNN: kd_tree, With LR Sig Cols kd_tree 30 20 0.612395 0.721943 0.686327 0.714717 0.638619 0.674800 00:00:04.069746
4 KNN: kd_tree, With LR Sig Cols kd_tree 30 30 0.624791 0.726968 0.683311 0.719745 0.652028 0.681369 00:00:04.494262
5 KNN: kd_tree, With LR Sig Cols kd_tree 30 40 0.633501 0.723283 0.685657 0.723098 0.659068 0.684921 00:00:04.957211
6 KNN: kd_tree, With LR Sig Cols kd_tree 30 50 0.629816 0.720268 0.689008 0.720751 0.665102 0.684989 00:00:05.234353
7 KNN: kd_tree, With LR Sig Cols kd_tree 30 100 0.644891 0.699162 0.704088 0.724103 0.685887 0.691626 00:00:06.251935
8 KNN: kd_tree, With LR Sig Cols kd_tree 30 150 0.643216 0.694807 0.710791 0.727791 0.696614 0.694644 00:00:06.918297
9 KNN: kd_tree, With LR Sig Cols kd_tree 30 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:07.477122
10 KNN: kd_tree, With LR Sig Cols kd_tree 30 250 0.667002 0.704188 0.701072 0.704995 0.685551 0.692562 00:00:07.902756
11 KNN: kd_tree, With LR Sig Cols kd_tree 2 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:09.582778
12 KNN: kd_tree, With LR Sig Cols kd_tree 3 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:07.722217
13 KNN: kd_tree, With LR Sig Cols kd_tree 4 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:07.744119
14 KNN: kd_tree, With LR Sig Cols kd_tree 5 200 0.656951 0.698492 0.702078 0.716058 0.691921 0.693100 00:00:07.730046
15 KNN: kd_tree, With LR Sig Cols kd_tree 20 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:07.274289
16 KNN: kd_tree, With LR Sig Cols kd_tree 50 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:07.642394
17 KNN: kd_tree, With LR Sig Cols kd_tree 100 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:07.881207
18 KNN: kd_tree, With LR Sig Cols kd_tree 150 200 0.656951 0.698827 0.702078 0.716058 0.691921 0.693167 00:00:07.781530
In [151]:
display(TopResultsDF)

plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))

FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
ModelVersion Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy
0 Random Forest: With LR Sig Cols 0.716918 0.756784 0.724531 0.748575 0.716728 0.732707
1 KNN: ball_tree, With LR Sig Cols 0.643216 0.694807 0.710791 0.727791 0.696614 0.694644
2 KNN: kd_tree, With LR Sig Cols 0.643216 0.694807 0.710791 0.727791 0.696614 0.694644
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [152]:
%%time

rfc_clf = RandomForestClassifier(n_estimators       =50, 
                                 max_features       = 5, 
                                 max_depth          = 4.0, 
                                 min_samples_split  = 18, 
                                 min_samples_leaf   = 9,
                                 n_jobs             = -1, 
                                 random_state       = seed) # get object
    
rfc_clf, rfc_acc = compute_kfold_scores_ClassificationBinary(rfc_clf, 
                                                             ##PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed),
                                                             cols = LRSigCols)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          858   642  1500
SC          209  1276  1485
All        1067  1918  2985

Normalized confusion matrix
[[ 0.572       0.428     ]
 [ 0.14074074  0.85925926]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS         1315   185  1500
SC          589   896  1485
All        1904  1081  2985

Normalized confusion matrix
[[ 0.87666667  0.12333333]
 [ 0.396633    0.603367  ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          844   655  1499
SC          156  1329  1485
All        1000  1984  2984

Normalized confusion matrix
[[ 0.56304203  0.43695797]
 [ 0.10505051  0.89494949]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          924   575  1499
SC          172  1312  1484
All        1096  1887  2983

Normalized confusion matrix
[[ 0.61641094  0.38358906]
 [ 0.11590296  0.88409704]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix
Predicted    NS    SC   All
True                       
NS          907   592  1499
SC          228  1256  1484
All        1135  1848  2983

Normalized confusion matrix
[[ 0.60507005  0.39492995]
 [ 0.15363881  0.84636119]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.71491, 0.7407, 0.72822, 0.74958, 0.72511]

        Average Accuracy: 0.7317

CPU times: user 8.94 s, sys: 2.74 s, total: 11.7 s
Wall time: 6.38 s
In [153]:
%%time
import os
from sklearn import tree
import pydotplus
import six
from sklearn.tree import export_graphviz
from IPython.display import SVG 



i_tree = 0
for tree_in_forest in rfc_clf.estimators_:       
    svgData = tree.export_graphviz(tree_in_forest, 
                         feature_names=fullColumns,
                         class_names=["NS", "SC"],
                         filled=True,
                         #rounded=True,
                         rotate = True,
                         label = 'All',
                         out_file=None)


    graph=pydotplus.graph_from_dot_data(svgData)
    
    if not os.path.exists('images'):
        os.makedirs('images')
    
    graph.write_svg('images/tree'+ str(i_tree) +'.svg')
        
    i_tree = i_tree + 1
CPU times: user 11.8 s, sys: 1.4 s, total: 13.2 s
Wall time: 16.2 s
In [154]:
SVG(filename='images/tree0.svg') 
Out[154]:
Tree 0 YearsToRetirement <= 0.3967 0.5 7521 [6003, 5934] NS 1 GSEGRD <= 0.4375 0.426 4783 [2330, 5245] SC 0->1 True 16 AGELVL_G <= 0.5 0.266 2738 [3673, 689] NS 0->16 False 2 YearsToRetirement <= 0.232 0.2825 1050 [289, 1409] SC 1->2 9 YearsToRetirement <= 0.235 0.4534 3733 [2041, 3836] SC 1->9 3 YearsToRetirement <= 0.1374 0.2018 718 [133, 1035] SC 2->3 6 BLS_FEDERAL_JobOpenings_Rate <= 0.5 0.4154 332 [156, 374] SC 2->6 4 0.1608 438 [64, 662] SC 3->4 5 0.2635 280 [69, 373] SC 3->5 7 0.4357 281 [144, 305] SC 6->7 8 0.2524 51 [12, 69] SC 6->8 10 BLS_FEDERAL_JobOpenings_Rate <= 0.5 0.3525 1382 [502, 1696] SC 9->10 13 IndAvgSalaryLog <= 0.5 0.4867 2351 [1539, 2140] SC 9->13 11 0.3489 1160 [416, 1432] SC 10->11 12 0.3707 222 [86, 264] SC 10->12 14 0.4855 2302 [1496, 2109] SC 13->14 15 0.4869 49 [43, 31] NS 13->15 17 BLS_FEDERAL_OtherSep_Level <= 0.5 0.2629 2728 [3671, 677] NS 16->17 24 0.2449 10 [2, 12] SC 16->24 18 AGELVL_J <= 0.5 0.2392 2550 [3503, 565] NS 17->18 21 SalaryOverUnderIndAvg <= 0.1667 0.48 178 [168, 112] NS 17->21 19 0.2297 2364 [3290, 502] NS 18->19 20 0.3523 186 [213, 63] NS 18->20 22 0.3702 34 [40, 13] NS 21->22 23 0.4918 144 [128, 99] NS 21->23
In [155]:
display(FinalResultsDF)
#plot top results across all model tests 
plot = FinalResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Final Results across Model Types",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
del FinalResultsDF
ModelVersion Iteration 0 Iteration 1 Iteration 2 Iteration 3 Iteration 4 MeanAccuracy
0 Logistic Regression: Manual Significant Features 0.685427 0.801675 0.732909 0.756956 0.747234 0.744840
1 Random Forest: With LR Sig Cols 0.716918 0.756784 0.724531 0.748575 0.716728 0.732707
2 KNN: kd_tree, Full Raw Columns 0.695477 0.762814 0.706099 0.741871 0.725779 0.726408
3 Random Forest: Top 15 Raw from PC 0.639866 0.723953 0.724531 0.762320 0.743882 0.718910
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [ ]:
 

Feature Importance of fit models

Binary Classifier Logistic Regression

In [156]:
print(lr_clf.coef_[0])
coef = pd.Series(lr_clf.coef_[0], index=LRSigCols)

maxcoef = pd.Series(pd.DataFrame(abs(coef).sort_values(ascending=False).head(20)).index)
       
weightsplot = pd.Series(coef, index=maxcoef)
weightsplot.plot(title = "Logistic Regression Coefficients", kind='bar', color = 'Tomato')
[-0.56018085 -2.0337071   0.79246957  0.40037421 -6.53055952 -0.26595873
 -0.53695709 -0.14721707  0.31296495  0.52654806  0.76167551  0.88364979
  0.70393248  0.57157599  0.47180908  0.46789742 -0.39240785  0.71680131
  0.3052245  -0.20092687 -0.25862654  0.84650795  0.30927242 -0.0796782
  0.38214948  0.82906346  0.32705161 -0.65200343  0.3441724   1.15162806
  1.37398542 -0.51255721]
Out[156]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5fd2514940>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

Define Model Fit on full train Professional Data

Using the full datset, to create our model fit allows us to fully utilize our dataset instead of simply utilizing the last 80% training fold fit on external data.

In [157]:
y = OPMAnalysisDataNoFamBinary["SEP"].values # get the labels we want    
y = np.where(y=="NS",0,1) # turn into numeric binary
X = OPMAnalysisDataNoFamBinary.drop("SEP", axis=1)

XFC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[fullColumns].as_matrix() 
XPCC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[PCList]#.as_matrix() 
XSigC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[LRSigCols].as_matrix() 
In [158]:
rfc_clf = RandomForestClassifier(n_estimators       =50, 
                                 max_features       = 5, 
                                 max_depth          = 4.0, 
                                 min_samples_split  = 18, 
                                 min_samples_leaf   = 9,
                                 n_jobs             = -1, 
                                 random_state       = seed) # get object

rfc_clf.fit(XSigC,y)
Out[158]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=4.0, max_features=5, max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=9,
            min_samples_split=18, min_weight_fraction_leaf=0.0,
            n_estimators=50, n_jobs=-1, oob_score=False,
            random_state=14920, verbose=0, warm_start=False)
In [159]:
knn_clf = KNeighborsClassifier(n_neighbors = 250, algorithm = 'kd_tree',leaf_size = 30, n_jobs=-1) # get object

knn_clf.fit(XPCC,y)
Out[159]:
KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=-1, n_neighbors=250, p=2,
           weights='uniform')
In [160]:
lr_clf = LogisticRegression(penalty='l2', C=1, class_weight=None, random_state=seed) # get object

lr_clf.fit(XSigC,y)
Out[160]:
LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=14920, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Predicting Admin from the Professional Model

In [161]:
%%time

if os.path.isfile(PickleJarPath+"/OPMAnalysisDataNoFamAdminBinary.pkl"):
    print("Found the File! Loading Pickle Now!")
    OPMAnalysisDataNoFamAdminBinary = unpickleObject("OPMAnalysisDataNoFamAdminBinary")
else:

    OPMAnalysisDataNoFamAdminBinary = SampledOPMDataAdmin.copy()

    cols = ["GENDER",
            "DATECODE",
            "QTR",
            "COUNT",
            "AGYTYPT",
            "AGYT",
            "AGYSUB",
            "AGYSUBT",
            "QTR",
            "AGELVLT",
            "LOSLVL",
            "LOSLVLT",
            "LOCTYPT",
            "LOCT",
            "OCCTYP",
            "OCCTYPT",
            "OCCFAM",
            "OCCFAMT",
            "OCC",
            "OCCT",
            "PATCO",
            "PPGRD",
            "PATCOT",
            "PPTYPT",
            "PPGROUPT",
            "PAYPLAN",
            "PAYPLANT",
            "SALLVLT",
            "TOATYPT",
            "TOAT",
            "WSTYP",
            "WSTYPT",
            "WORKSCH",
            "WORKSCHT",
            "SALARY",
            "LOS",
            "SEPCount_EFDATE_OCC",
            "SEPCount_EFDATE_LOC"
           ]



    #delete cols from analysis data
    for col in cols:
        if col in list(OPMAnalysisDataNoFamAdminBinary.columns):
            del OPMAnalysisDataNoFamAdminBinary[col]

    OPMAnalysisDataNoFamAdminBinary.info()

    cols = ["AGELVL",
            "LOC",
            "SALLVL",
            "TOA",
            "AGYTYP",
            "AGY",
            "LOCTYP",
            "PPTYP",
            "PPGROUP",
            "TOATYP"
           ]

    #Split Values for cols 
    for col in cols:
        if col in list(OPMAnalysisDataNoFamAdminBinary.columns):
            AttSplit = pd.get_dummies(OPMAnalysisDataNoFamAdminBinary[col],prefix=col)
            display(AttSplit.head())
            OPMAnalysisDataNoFamAdminBinary = pd.concat((OPMAnalysisDataNoFamAdminBinary,AttSplit),axis=1) # add back into the dataframe
            del OPMAnalysisDataNoFamAdminBinary[col]

    pickleObject(OPMAnalysisDataNoFamAdminBinary, "OPMAnalysisDataNoFamAdminBinary")
        
display(OPMAnalysisDataNoFamAdminBinary.head())
print("Number of Columns: ",len(OPMAnalysisDataNoFamAdminBinary.columns))
OPMAnalysisDataNoFamAdminBinary.info()
Found the File! Loading Pickle Now!
SEP GSEGRD IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog AGELVL_B AGELVL_C AGELVL_D AGELVL_E AGELVL_F AGELVL_G AGELVL_H AGELVL_I AGELVL_J AGELVL_K LOC_01 LOC_02 LOC_04 LOC_05 LOC_06 LOC_08 LOC_09 LOC_10 LOC_11 LOC_12 LOC_13 LOC_15 LOC_16 LOC_17 LOC_18 LOC_19 LOC_20 LOC_21 LOC_22 LOC_23 LOC_24 LOC_25 LOC_26 LOC_27 LOC_28 LOC_29 LOC_30 LOC_31 LOC_32 LOC_33 LOC_34 LOC_35 LOC_36 LOC_37 LOC_38 LOC_39 LOC_40 LOC_41 LOC_42 LOC_44 LOC_45 LOC_46 LOC_47 LOC_48 LOC_49 LOC_50 LOC_51 LOC_53 LOC_54 LOC_55 LOC_56 TOA_10 TOA_15 TOA_20 TOA_30 TOA_32 TOA_35 TOA_38 TOA_40 TOA_42 TOA_44 TOA_45 TOA_48 LOCTYP_1 PPTYP_1 PPGROUP_11 PPGROUP_12 TOATYP_1 TOATYP_2
0 NS 7.0 42440.609454 -928.609454 20.0 37.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 10.633738 0.316228 4.174387 5.365976 10.655861 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0
1 NS 9.0 59811.047401 -373.047401 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 10.992689 2.469818 4.905275 6.265301 10.998946 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
2 NS 9.0 54720.390255 1115.609745 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 10.930174 1.183216 4.905275 5.117994 10.909992 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
3 NS 11.0 66557.448029 -5431.448029 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.020693 2.144761 5.627621 6.745236 11.105821 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
4 NS 11.0 70834.469577 -7743.469577 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.052333 1.673320 6.148468 6.827629 11.168101 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
Number of Columns:  100
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14918 entries, 0 to 14917
Data columns (total 100 columns):
SEP                              14918 non-null object
GSEGRD                           14918 non-null float64
IndAvgSalary                     14918 non-null float64
SalaryOverUnderIndAvg            14918 non-null float64
LowerLimitAge                    14918 non-null float64
YearsToRetirement                14918 non-null float64
BLS_FEDERAL_OtherSep_Rate        14918 non-null float64
BLS_FEDERAL_Quits_Rate           14918 non-null float64
BLS_FEDERAL_TotalSep_Level       14918 non-null int64
BLS_FEDERAL_JobOpenings_Rate     14918 non-null float64
BLS_FEDERAL_OtherSep_Level       14918 non-null int64
BLS_FEDERAL_Quits_Level          14918 non-null int64
BLS_FEDERAL_JobOpenings_Level    14918 non-null int64
BLS_FEDERAL_Layoffs_Rate         14918 non-null float64
BLS_FEDERAL_Layoffs_Level        14918 non-null int64
BLS_FEDERAL_TotalSep_Rate        14918 non-null float64
SALARYLog                        14918 non-null float64
LOSSqrt                          14918 non-null float64
SEPCount_EFDATE_OCCLog           14918 non-null float64
SEPCount_EFDATE_LOCLog           14918 non-null float64
IndAvgSalaryLog                  14918 non-null float64
AGELVL_B                         14918 non-null uint8
AGELVL_C                         14918 non-null uint8
AGELVL_D                         14918 non-null uint8
AGELVL_E                         14918 non-null uint8
AGELVL_F                         14918 non-null uint8
AGELVL_G                         14918 non-null uint8
AGELVL_H                         14918 non-null uint8
AGELVL_I                         14918 non-null uint8
AGELVL_J                         14918 non-null uint8
AGELVL_K                         14918 non-null uint8
LOC_01                           14918 non-null uint8
LOC_02                           14918 non-null uint8
LOC_04                           14918 non-null uint8
LOC_05                           14918 non-null uint8
LOC_06                           14918 non-null uint8
LOC_08                           14918 non-null uint8
LOC_09                           14918 non-null uint8
LOC_10                           14918 non-null uint8
LOC_11                           14918 non-null uint8
LOC_12                           14918 non-null uint8
LOC_13                           14918 non-null uint8
LOC_15                           14918 non-null uint8
LOC_16                           14918 non-null uint8
LOC_17                           14918 non-null uint8
LOC_18                           14918 non-null uint8
LOC_19                           14918 non-null uint8
LOC_20                           14918 non-null uint8
LOC_21                           14918 non-null uint8
LOC_22                           14918 non-null uint8
LOC_23                           14918 non-null uint8
LOC_24                           14918 non-null uint8
LOC_25                           14918 non-null uint8
LOC_26                           14918 non-null uint8
LOC_27                           14918 non-null uint8
LOC_28                           14918 non-null uint8
LOC_29                           14918 non-null uint8
LOC_30                           14918 non-null uint8
LOC_31                           14918 non-null uint8
LOC_32                           14918 non-null uint8
LOC_33                           14918 non-null uint8
LOC_34                           14918 non-null uint8
LOC_35                           14918 non-null uint8
LOC_36                           14918 non-null uint8
LOC_37                           14918 non-null uint8
LOC_38                           14918 non-null uint8
LOC_39                           14918 non-null uint8
LOC_40                           14918 non-null uint8
LOC_41                           14918 non-null uint8
LOC_42                           14918 non-null uint8
LOC_44                           14918 non-null uint8
LOC_45                           14918 non-null uint8
LOC_46                           14918 non-null uint8
LOC_47                           14918 non-null uint8
LOC_48                           14918 non-null uint8
LOC_49                           14918 non-null uint8
LOC_50                           14918 non-null uint8
LOC_51                           14918 non-null uint8
LOC_53                           14918 non-null uint8
LOC_54                           14918 non-null uint8
LOC_55                           14918 non-null uint8
LOC_56                           14918 non-null uint8
TOA_10                           14918 non-null uint8
TOA_15                           14918 non-null uint8
TOA_20                           14918 non-null uint8
TOA_30                           14918 non-null uint8
TOA_32                           14918 non-null uint8
TOA_35                           14918 non-null uint8
TOA_38                           14918 non-null uint8
TOA_40                           14918 non-null uint8
TOA_42                           14918 non-null uint8
TOA_44                           14918 non-null uint8
TOA_45                           14918 non-null uint8
TOA_48                           14918 non-null uint8
LOCTYP_1                         14918 non-null uint8
PPTYP_1                          14918 non-null uint8
PPGROUP_11                       14918 non-null uint8
PPGROUP_12                       14918 non-null uint8
TOATYP_1                         14918 non-null uint8
TOATYP_2                         14918 non-null uint8
dtypes: float64(15), int64(5), object(1), uint8(79)
memory usage: 3.5+ MB
CPU times: user 95.4 ms, sys: 2.9 ms, total: 98.3 ms
Wall time: 93.7 ms
In [162]:
if os.path.isfile(PickleJarPath+"/OPMAnalysisScalerFit.pkl"):
    print("Found the File! Loading Pickle Now!")
    OPMAnalysisScalerFit = unpickleObject("OPMAnalysisScalerFit")
Found the File! Loading Pickle Now!
In [163]:
#OPMAnalysisDataNoFamAdminBinary = OPMAnalysisDataNoFamAdmin[(OPMAnalysisDataNoFamAdmin["SEP"] == 'NS') | (OPMAnalysisDataNoFamAdmin["SEP"] == 'SC')].reset_index()
OPMAnalysisDataNoFamAdminBinaryScaled = OPMAnalysisDataNoFamAdminBinary[OPMScaledAnalysisData.columns]

print(OPMAnalysisDataNoFamAdminBinaryScaled.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14918 entries, 0 to 14917
Data columns (total 99 columns):
GSEGRD                           14918 non-null float64
IndAvgSalary                     14918 non-null float64
SalaryOverUnderIndAvg            14918 non-null float64
LowerLimitAge                    14918 non-null float64
YearsToRetirement                14918 non-null float64
BLS_FEDERAL_OtherSep_Rate        14918 non-null float64
BLS_FEDERAL_Quits_Rate           14918 non-null float64
BLS_FEDERAL_TotalSep_Level       14918 non-null int64
BLS_FEDERAL_JobOpenings_Rate     14918 non-null float64
BLS_FEDERAL_OtherSep_Level       14918 non-null int64
BLS_FEDERAL_Quits_Level          14918 non-null int64
BLS_FEDERAL_JobOpenings_Level    14918 non-null int64
BLS_FEDERAL_Layoffs_Rate         14918 non-null float64
BLS_FEDERAL_Layoffs_Level        14918 non-null int64
BLS_FEDERAL_TotalSep_Rate        14918 non-null float64
SALARYLog                        14918 non-null float64
LOSSqrt                          14918 non-null float64
SEPCount_EFDATE_OCCLog           14918 non-null float64
SEPCount_EFDATE_LOCLog           14918 non-null float64
IndAvgSalaryLog                  14918 non-null float64
AGELVL_B                         14918 non-null uint8
AGELVL_C                         14918 non-null uint8
AGELVL_D                         14918 non-null uint8
AGELVL_E                         14918 non-null uint8
AGELVL_F                         14918 non-null uint8
AGELVL_G                         14918 non-null uint8
AGELVL_H                         14918 non-null uint8
AGELVL_I                         14918 non-null uint8
AGELVL_J                         14918 non-null uint8
AGELVL_K                         14918 non-null uint8
LOC_01                           14918 non-null uint8
LOC_02                           14918 non-null uint8
LOC_04                           14918 non-null uint8
LOC_05                           14918 non-null uint8
LOC_06                           14918 non-null uint8
LOC_08                           14918 non-null uint8
LOC_09                           14918 non-null uint8
LOC_10                           14918 non-null uint8
LOC_11                           14918 non-null uint8
LOC_12                           14918 non-null uint8
LOC_13                           14918 non-null uint8
LOC_15                           14918 non-null uint8
LOC_16                           14918 non-null uint8
LOC_17                           14918 non-null uint8
LOC_18                           14918 non-null uint8
LOC_19                           14918 non-null uint8
LOC_20                           14918 non-null uint8
LOC_21                           14918 non-null uint8
LOC_22                           14918 non-null uint8
LOC_23                           14918 non-null uint8
LOC_24                           14918 non-null uint8
LOC_25                           14918 non-null uint8
LOC_26                           14918 non-null uint8
LOC_27                           14918 non-null uint8
LOC_28                           14918 non-null uint8
LOC_29                           14918 non-null uint8
LOC_30                           14918 non-null uint8
LOC_31                           14918 non-null uint8
LOC_32                           14918 non-null uint8
LOC_33                           14918 non-null uint8
LOC_34                           14918 non-null uint8
LOC_35                           14918 non-null uint8
LOC_36                           14918 non-null uint8
LOC_37                           14918 non-null uint8
LOC_38                           14918 non-null uint8
LOC_39                           14918 non-null uint8
LOC_40                           14918 non-null uint8
LOC_41                           14918 non-null uint8
LOC_42                           14918 non-null uint8
LOC_44                           14918 non-null uint8
LOC_45                           14918 non-null uint8
LOC_46                           14918 non-null uint8
LOC_47                           14918 non-null uint8
LOC_48                           14918 non-null uint8
LOC_49                           14918 non-null uint8
LOC_50                           14918 non-null uint8
LOC_51                           14918 non-null uint8
LOC_53                           14918 non-null uint8
LOC_54                           14918 non-null uint8
LOC_55                           14918 non-null uint8
LOC_56                           14918 non-null uint8
TOA_10                           14918 non-null uint8
TOA_15                           14918 non-null uint8
TOA_20                           14918 non-null uint8
TOA_30                           14918 non-null uint8
TOA_32                           14918 non-null uint8
TOA_35                           14918 non-null uint8
TOA_38                           14918 non-null uint8
TOA_40                           14918 non-null uint8
TOA_42                           14918 non-null uint8
TOA_44                           14918 non-null uint8
TOA_45                           14918 non-null uint8
TOA_48                           14918 non-null uint8
LOCTYP_1                         14918 non-null uint8
PPTYP_1                          14918 non-null uint8
PPGROUP_11                       14918 non-null uint8
PPGROUP_12                       14918 non-null uint8
TOATYP_1                         14918 non-null uint8
TOATYP_2                         14918 non-null uint8
dtypes: float64(15), int64(5), uint8(79)
memory usage: 3.4 MB
None

Predicting Admin with Random Forest

In [164]:
%%time
OPMAnalysisDataNoFamAdminBinaryScaled = pd.DataFrame(OPMAnalysisScalerFit.transform(OPMAnalysisDataNoFamAdminBinaryScaled), columns = OPMAnalysisDataNoFamAdminBinaryScaled.columns)

print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", rfc_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))

results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": rfc_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")

display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())

print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))

    # Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum), 
                      classes   =["NS",  "SC"], 
                      normalize =True,
                      title     ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model:  0.724225767529
SEP Cnt
0 NS 7495
1 SC 7423
SEP PredictTxt Cnt
0 NS NS 4697
1 NS SC 2798
2 SC NS 1316
3 SC SC 6107
confusion matrix
Predicted    NS    SC    All
True                        
NS         4697  1316   6013
SC         2798  6107   8905
All        7495  7423  14918

Normalized confusion matrix
[[ 0.78114086  0.21885914]
 [ 0.3142055   0.6857945 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 653 ms, sys: 302 ms, total: 955 ms
Wall time: 613 ms

Predicting Admin with KNN

In [165]:
%%time

print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", knn_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[PCList], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))

results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": knn_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[PCList])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")

display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())

print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))

    # Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum), 
                      classes   =["NS",  "SC"], 
                      normalize =True,
                      title     ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model:  0.709880681056
SEP Cnt
0 NS 7495
1 SC 7423
SEP PredictTxt Cnt
0 NS NS 5554
1 NS SC 1941
2 SC NS 2387
3 SC SC 5036
confusion matrix
Predicted    NS    SC    All
True                        
NS         5554  2387   7941
SC         1941  5036   6977
All        7495  7423  14918

Normalized confusion matrix
[[ 0.69940813  0.30059187]
 [ 0.2781998   0.7218002 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 17min 44s, sys: 1.27 s, total: 17min 46s
Wall time: 25.3 s

Predicting Admin with Logistic Regression

In [166]:
%%time

print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", lr_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))

results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": lr_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")

display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())

print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))

    # Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum), 
                      classes   =["NS",  "SC"], 
                      normalize =True,
                      title     ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model:  0.726035661617
SEP Cnt
0 NS 7495
1 SC 7423
SEP PredictTxt Cnt
0 NS NS 5291
1 NS SC 2204
2 SC NS 1883
3 SC SC 5540
confusion matrix
Predicted    NS    SC    All
True                        
NS         5291  1883   7174
SC         2204  5540   7744
All        7495  7423  14918

Normalized confusion matrix
[[ 0.73752439  0.26247561]
 [ 0.28460744  0.71539256]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1.08 s, sys: 4.76 s, total: 5.84 s
Wall time: 375 ms
In [185]:
#OPMAnalysisDataNoFamBinary.loc[:, OPMAnalysisDataNoFamBinary.max() != 1].head()
Out[185]:
SEP GSEGRD IndAvgSalary SalaryOverUnderIndAvg LowerLimitAge YearsToRetirement BLS_FEDERAL_OtherSep_Rate BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
0 NS 11.0 65898.205859 -4041.205859 20.0 37.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.032581 2.167948 5.817111 6.152733 11.095866
1 NS 12.0 81218.917413 -9405.917413 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.181821 2.683282 5.817111 6.240276 11.304903
2 NS 11.0 65898.205859 -2807.205859 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.052333 2.000000 5.817111 6.827629 11.095866
3 NS 12.0 82168.243394 -6547.243394 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.233489 2.408319 4.317488 6.827629 11.316524
4 NS 13.0 121938.733696 -26019.733696 25.0 32.0 0.4 0.4 34 2.1 10 11 58 0.5 13 1.2 11.471259 1.414214 4.143135 6.827629 11.711274
In [341]:
plotCols = ["SEP", "LOSSqrt", "LowerLimitAge", "GSEGRD", "BLS_FEDERAL_JobOpenings_Level"]

vizyDataNonBin = OPMAnalysisDataNoFamBinary[plotCols].copy()

#Random matrix doesn't work since all our variables on different scale
#noise = np.random.normal(0,0.2,[len(vizyDataNonBin), len(vizyDataNonBin.columns)-2])
#vizyDataNonBin.iloc[:,2:] = vizyDataNonBin.drop(["SEP", "LOSSqrt"], axis=1) + noise

vizyDataNonBin.LowerLimitAge += np.random.normal(0,2,len(vizyDataNonBin))
vizyDataNonBin.GSEGRD += np.random.normal(0,0.2,len(vizyDataNonBin))
vizyDataNonBin.BLS_FEDERAL_JobOpenings_Level += np.random.normal(0,1,len(vizyDataNonBin))
#vizyDataNonBin.TOA_44 += np.random.normal(0,0.1,len(vizyDataNonBin))
#vizyDataNonBin.TOA_42 += np.random.normal(0,0.1,len(vizyDataNonBin))
#vizyDataNonBin.LOC_46 += np.random.normal(0,0.1,len(vizyDataNonBin))

vizyDataNonBin.head()
Out[341]:
SEP LOSSqrt LowerLimitAge GSEGRD BLS_FEDERAL_JobOpenings_Level
0 NS 2.167948 19.300773 10.930295 58.780536
1 NS 2.683282 23.989860 11.741308 59.279524
2 NS 2.000000 20.773648 10.896618 58.932700
3 NS 2.408319 23.045929 12.425405 59.311978
4 NS 1.414214 25.919086 13.020518 56.394233
In [342]:
%%time

#plotCols = ["SEP", "LOSSqrt", "LowerLimitAge", "BLS_FEDERAL_OtherSep_Rate", "GSEGRD", "BLS_FEDERAL_JobOpenings_Level"]

#mapping = {'NS':0, 'SC':1}
#y = OPMAnalysisDataNoFamBinary.replace({'SEP': mapping})

#y = vizyDataNonBin[plotCols]
#vizyDataNonBin = vizyDataNonBin.replace({'SEP': mapping})

#sns.set(font_scale=1)
##sns.pairplot(vizyDataNonBin[LRSigCols], palette="hls", plot_kws={"s": 50})
#sns.pairplot(vizyDataNonBin, hue="SEP", palette="hls", plot_kws={"s": 10})

g = sns.PairGrid(vizyDataNonBin, hue='SEP', palette='hls')
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter, s=10, edgecolor="white", alpha=0.1, lw=0)
g.add_legend()
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 3.65 s, sys: 3 s, total: 6.65 s
Wall time: 3.02 s
In [406]:
%%time

plotCols = list(OPMAnalysisDataNoFamBinary[LRSigCols].loc[:,OPMAnalysisDataNoFamBinary[LRSigCols].max() == 1].columns)
plotCols.extend(("LOSSqrt", "SEP"))

vizyDataBin = OPMAnalysisDataNoFamBinary[plotCols].copy()

sns.set(style="whitegrid", palette="pastel", color_codes=True)

for i, col in enumerate(vizyDataBin.iloc[:,:-2]):
    plt.figure(i).set_size_inches(11, 8)
    sns.violinplot(x=col, y="LOSSqrt", hue="SEP", data=vizyDataBin, split=True,
                   inner="quart", palette={"NS": "g", "SC": "r"}, scale = 'count')
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
CPU times: user 4.14 s, sys: 15.8 ms, total: 4.16 s
Wall time: 4.16 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [405]:
vizyDataBin.groupby(["TOA_44", "SEP"]).size().reset_index(name="Time")
Out[405]:
TOA_44 SEP Time
0 0 NS 7496
1 0 SC 7414
2 1 NS 1
3 1 SC 9